Deconstructing the Rust Discourse


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Hodong Kim <hodong@nimfsoft.art>

Preface

This book aims to analyze specific technical and social discourses surrounding the Rust programming language. The subjects of analysis include claims such as “Memory safety can only be achieved with Rust” or “C++ is no longer a modern systems programming language.” This book goes beyond merely explaining the language’s syntax and features to investigate the engineering trade-offs (design exchange relationships where gaining one thing requires partially giving up another) that have shaped Rust’s core principles of ‘safety’, ‘performance’, and ‘concurrency’, and to examine the historical and technical contexts of these concepts.

To this end, this book includes discussions on the design and history of several programming languages, including C++, Ada, and Go. Therefore, this book is intended for an audience with an understanding of various systems programming paradigms and the fundamental principles of computer science.

In particular, this book frequently references Ada and its subset SPARK as a means of comparison, in addition to C++, which is the primary comparison in the Rust discourse. Ada/SPARK is used as an analytical means of comparison to show how the same goal of ‘safety’ can be achieved through different philosophies and engineering compromises. Through this, it analyzes from multiple perspectives how Rust’s design method is one of several possible approaches, not the only solution, and aims to present historical precedents for core principles such as ‘memory safety without a garbage collector (GC)1’. This approach expands the scope of technological evaluation beyond the dichotomous framework with C++ formed by the mainstream discourse and contributes to evaluating Rust’s engineering achievements and limitations according to objective criteria.

It is clarified that the subject of this book’s analysis is not the Rust technology itself, nor the official positions of the Rust Foundation and its core development teams. The official channels of the Rust project recognize the technical challenges discussed in this book as areas for improvement and are exploring solutions. This book analyzes not these official activities, but the formation and diffusion of specific discourses observed on some online technical forums and social media. Therefore, this analysis is not an evaluation of any specific group but is intended to provide an understanding of the discourse structure of a technical ecosystem. In this book, ‘the Rust discourse’ refers not to the consensus of the entire community, but to the specific tendencies selected for analysis.

This book has no intention of devaluing Rust’s technical achievements. Its premise is that because Rust is a widely adopted technology, a detailed and multi-faceted discussion is necessary. The purpose of this book is not to support or criticize a specific technology, but to contribute to developers forming a comprehensive technical perspective by objectively analyzing engineering trade-offs and the formation process of technical discourse.

Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Table of Contents

Part 1: The Rise of Rust, A Narrative of Technical Innovation and Success

1. Introduction to the Rust Language and Its Key Features

1.1 Origin: Addressing the Trade-off Between ‘Performance’ and ‘Safety’

While programming languages are developed for various reasons, Rust gained attention by approaching existing paradigms in a new way. To understand Rust’s origin, it is necessary to examine the long-discussed choices within the field of systems programming.

In the past, developers of low-level systems were faced with a choice between ‘performance’ and ‘safety.’ On one side were languages like C/C++, which provided high performance and control by directly managing hardware, but assigned the responsibility of handling memory errors such as segmentation faults, buffer overflows, and data races to the programmer. On the other side were languages like Ada, which pursued a high level of safety and predictability at the language level and were used in specific high-reliability systems. Another example was garbage collector (GC)-based languages like Java or C#, which provided memory safety through automatic memory management but were limited in some system domains like real-time systems or operating system kernels due to the GC’s runtime overhead and unpredictable behavior.

Started as a research project at Mozilla, Rust was developed with the goal of resolving the trade-off between ‘performance and safety.’ The aim was to create “a language that has C++-level performance while ensuring memory safety without a GC.” To achieve this, Rust established the core objectives of safety, performance, and concurrency from the early stages of its design.

Safety

One of Rust’s core design principles is memory safety. It aims to prevent issues such as abnormal program termination, data corruption, and system control hijacking that can arise from memory errors by checking memory usage rules at compile time. This is an approach where the compiler blocks the compilation of code that could cause errors, in order to reduce the possibility of programmer mistakes.

Performance

Rust is a systems programming language and considers performance one of its core objectives. It is designed to efficiently use hardware performance without relying on a runtime like a GC. The ‘Zero-Cost Abstractions’ principle represents Rust’s design philosophy of ensuring that no additional runtime cost is incurred even when developers use high-level abstraction features.

Concurrency

In a multi-core processor environment, writing code where multiple threads share data without conflict is a complex problem. Rust’s ownership system finds and prevents concurrency-related errors like ‘data races’ at compile time. Through this, developers can experience the concept of ‘fearless concurrency.’ Here, ‘fearless’ means that developers can write concurrent code based on the technical guarantee that certain types of bugs, such as data races, are prevented by the compiler.

In conclusion, the question ‘Why Rust?’ can be answered at the intersection of these three objectives. Rust is an attempt to implement the values of performance, safety, and concurrency—which were previously difficult to achieve simultaneously—within a single language. To achieve this goal, Rust introduced the concept of ‘ownership’ as a core feature of the language.

1.2 Memory Management through Ownership, Borrowing, and Lifetimes

Rust’s audacious goals, particularly ‘memory safety without a garbage collector (GC),’ were considered nearly impossible in existing programming languages. Relying on manual management like in C/C++ led to human error, while relying on a GC like in Java meant accepting runtime performance degradation. To solve this problem, Rust introduced a unique and core system that strictly enforces memory management rules not at runtime, but at compile time. This system is built on three concepts: ownership, borrowing, and lifetimes.

1. Ownership: Every Value Has an Owner

Rust’s memory management philosophy begins with a single, simple rule: ownership.

  • Every value has a single variable that is its owner.
  • When the owner goes out of scope, the value is automatically dropped (its memory is freed).
  • Ownership can be ‘moved’ to another variable; after the move, the original owner is no longer valid.

These three rules have a powerful effect. Since only one owner can drop a value, ‘double free’ errors are impossible by design. Furthermore, because the previous variable becomes unusable after ownership is moved, ‘use-after-free’ errors are also prevented at compile time.

2. Borrowing: Safe Access Without Ownership

If moving ownership were the only way to pass data, it would be highly inefficient and cumbersome, as ownership would constantly shift every time a value is passed to a function. To solve this, Rust provides the concept of ‘borrowing.’ This means temporarily lending out access (a reference) to data within a specific scope, without transferring ownership.

However, this ‘borrowing’ comes with strict rules that must be followed.

  • Multiple ‘immutable borrows’ (&T) to a piece of data can exist simultaneously.
  • However, only one ‘mutable borrow’ (&mut T) can exist, and no other borrows are allowed during its lifetime.

Through these rules, the compiler completely blocks any attempts to modify data from multiple places simultaneously or to read data while it is being modified. This is the core principle by which Rust prevents ‘data races’ and achieves ‘fearless concurrency.’

3. Lifetimes: Guaranteeing the Validity of Borrowed Data

If something is borrowed, a mechanism is needed to guarantee how long it remains valid. ‘Lifetimes’ serve this purpose by telling the compiler the scope for which a ‘borrow’ (a reference) is valid—its ‘lifespan.’

The compiler uses lifetime analysis to prevent ‘dangling pointer’ problems, which occur when borrowed data is dropped by its owner before the reference is. In other words, it never allows the dangerous situation where “the lifetime of a reference outlives the lifetime of the actual data.” While the compiler automatically infers lifetimes in most cases, developers can explicitly specify them in complex situations to aid the compiler’s analysis.

This sophisticated system of three concepts—managing a resource’s life with ownership, sharing it safely without data races via borrowing, and preventing dangling pointers with lifetimes—is enforced by a part of the compiler called the ‘borrow checker.’ While this strict checker is a primary cause of Rust’s steep learning curve, it is also the core mechanism that realizes the ‘safety without performance degradation’ that Rust so proudly touts.

1.3 The Lineage of Zero-Cost Abstractions

Providing High-Level Convenience and Low-Level Control Simultaneously

In the world of traditional programming languages, ‘level of abstraction’ and ‘performance’ have long been in a trade-off relationship. High-level languages like Python and Java offer powerful abstractions that are convenient for developers, but it was considered natural that using these features would incur invisible runtime overhead. Conversely, low-level languages like C provided near-hardware-level performance, but developers had to manage everything manually and endure the inconvenience of reduced code readability and maintainability. One had to choose between “beautiful code that is easy to read and write” and “fast performance.”

C++ and Rust offer a powerful philosophy in response to this long-standing trade-off: “You don’t pay for what you don’t use.” This is the principle of ‘Zero-Cost Abstractions (ZCA).’ ZCA dictates that even when a developer writes code using high-level, convenient abstractions like iterators, generics, and traits, the final compiled result must have the same performance as low-level, hand-optimized code.

The deepest roots of this principle can be found in C. C provided the foundation for programmers to create cost-free code manually, by allowing direct control over memory layout with struct and eliminating function call overhead with inline functions or macros. For instance, using the sizeof operator to calculate the precise memory size of a data structure at compile time, or using #define macros to expand repetitive code before compilation, can be seen as C’s primitive form of ZCA, achieving high-level convenience without runtime overhead.

C++ built upon this foundation, achieving an innovation by constructing ‘safe and extensible abstractions’ at the language level. The keys were templates and RAII (Resource Acquisition Is Initialization).

  • Templates automatically generated code for multiple types at compile time while ensuring type safety.
  • RAII automated resource management through destructors, fundamentally reducing programmer error.

Rust fully inherited this ZCA philosophy from C/C++ and added a powerful safety mechanism: ‘ownership.’ That is, it ensures performance by handling the cost of abstractions at compile time rather than runtime, while also forcing all abstractions to comply with memory safety rules through the borrow checker.

A prime example is the iterator. Consider the following code:

// Code to find the sum of the squares of numbers divisible by 3 from 1 to 99
let sum = (1..100).filter(|&x| x % 3 == 0).map(|x| x * x).sum::<u32>();

This code uses a chain of high-level methods like filter, map, and sum to clearly declare “what to do.” In C, this would have required a complex implementation using a for loop, an if condition, and a separate sum variable. However, the Rust compiler optimizes this high-level iterator code to generate machine code that is virtually indistinguishable in performance from a hand-written for loop. The overhead of intermediate calls like filter and map is completely eliminated during compilation, and on top of that, it is guaranteed that all memory access is safe at compile time.

This compile-time optimization is made possible by Rust’s powerful type system, generics, and aggressive compiler techniques like inlining and monomorphization. The compiler does more work so that the runtime user pays no cost.

1.4 Ensuring Safety Through the Type System and Pattern Matching

The Strictness of Catching Errors at Compile Time

The ‘safety’ that Rust pursues is not limited to just memory management. Rust is designed to explicitly express the various states and potential errors a program can encounter at the code level through its foundational static type system, and to have the compiler enforce this. This is a core strategy for maximizing the overall stability of a program by catching potential runtime errors at compile time. At the heart of this strategy are Rust’s powerful type system and the tool for effectively handling it: pattern matching.

The most brilliant part of Rust’s type system is the enum (enumeration). Unlike in other languages where enum is merely used to list a few constants, a Rust enum is a flexible data structure where each variant can hold different types and numbers of values. Rust leverages this to handle a program’s uncertain states in a very safe manner.

A prime example is the Option<T> type, which solves the null pointer problem. Rust has no null. Instead, a situation where a value might or might not exist is represented by the Option enum, which has two states: Some(value) or None. By doing this, the compiler forces the developer to handle the None case, thus blocking the possibility of runtime errors like ‘null pointer dereferencing’ at compile time. Similarly, operations that might succeed or fail are made to explicitly return a Result<T, E> type, with Ok(value) or Err(error) states, preventing the mistake of omitting error handling.

The tool that makes handling these powerful types safe and convenient is pattern matching. Rust’s match expression forces the compiler to check every possible case of an enum like Option or Result without omission. This is called exhaustiveness checking.

let maybe_number: Option<i32> = Some(10);

// The `match` expression forces an exhaustive check of all possible cases,
// a feature known as 'exhaustiveness checking'.
// Therefore, omitting the `None` case will result in a compile error.
match maybe_number {
    Some(number) => println!("The number is: {}", number),
    None => println!("There is no number."),
}

In this way, the compiler prevents the common mistake of a programmer forgetting to handle a particular state or error case by kindly pointing out, “You missed this case.”

In short, Rust’s powerful type system allows for the explicit modeling of a program’s state, and pattern matching enforces the safe and exhaustive handling of all those states. This is a prime example of Rust’s core design philosophy: “making the compiler a strict partner to eradicate numerous potential runtime bugs at compile time.”

1.5 The Ecosystem: Cargo and Crates.io

A Modern Build System and Package Manager

For any programming language to succeed, it needs not only the excellence of the language itself but also a powerful ecosystem and tools that help developers use it easily and efficiently. Traditional systems programming languages like C/C++, in particular, lacked an official package manager or build system, forcing developers to spend a great deal of time on different tools for each project (Makefile, CMake, etc.) and complex library dependency issues.

To solve these problems, Rust made providing a modern development environment one of its core goals from the very beginning of its design. At the center of this are Rust’s official build system and package manager, Cargo, and the official package repository, Crates.io.

Cargo is more than just a code compiler; it’s an all-in-one command-line tool that manages the entire lifecycle of a project. Developers can easily handle the following tasks with consistent commands:

  • Project Creation (cargo new): Creates a new project with a standardized directory structure.
  • Dependency Management: By simply specifying the name and version of required libraries (called ‘crates’ in Rust) in a configuration file named Cargo.toml, Cargo automatically downloads and manages those libraries and all their sub-dependencies.
  • Building and Running (cargo build, cargo run): Compiles and runs the project with a single command.
  • Testing and Documentation (cargo test, cargo doc): Runs the test code included in the project and generates clean HTML documentation based on source code comments.

At the heart of all these tasks is Crates.io. This is a centralized package repository, similar to Node.js’s NPM or Python’s PyPI, that serves as a platform for Rust developers worldwide to easily share the libraries they’ve created and use libraries made by others.

In conclusion, the Cargo and Crates.io ecosystem is one of the reasons many developers rate Rust as “highly productive,” despite its steep learning curve. By integrating the complex processes from project setup to dependency management, building, and testing into a single, standardized tool, it aims to lower the complexity of setting up a development environment and managing dependencies, allowing developers to focus solely on the code itself.

2. The Drivers of Rust’s Success: A Fusion of Technology, Ecosystem, and Narrative

In a fierce programming language market where countless new languages have appeared and vanished over decades, how did Rust succeed in achieving high developer preference and strategic adoption by major tech companies in such a short period? To answer this question, we must look beyond Rust’s technical flaws or discourse issues and conduct an in-depth analysis of the complex drivers that propelled its success.

Rust’s success cannot be explained by a single factor; it is the result of a delicate interplay of technical justification, an innovative developer experience, a powerful narrative, and the demands of the era. This chapter will analyze these key drivers to elucidate how Rust came to occupy a significant position in the modern software development ecosystem.

2.1 Technical Justification: A Solution to a “Seemingly Unsolvable Problem”

The most fundamental driver of Rust’s success lies in its presentation of a practical and powerful solution to a long-standing challenge in systems programming: ‘memory safety without performance degradation.’

For decades, developers had to accept the chronic risk of memory errors to gain the high performance and direct hardware control offered by C/C++. On the other hand, garbage-collected (GC) languages like Java and C# provided memory safety, but their unpredictable ‘stop-the-world’ pauses and runtime overhead prevented them from replacing all systems domains, such as operating systems, browser engines, and real-time systems.

Rust directly confronted this dilemmatic structure. Through a compile-time static analysis model featuring ownership and the borrow checker, it opened a path to preventing fatal memory errors at their source without a GC, all while maintaining runtime performance comparable to C++. This was not a mere technical improvement but a provision of technical justification that shattered the existing paradigm that “safety and performance are a trade-off.” Especially as the industry’s demand for memory safety reached its peak following major security incidents like Heartbleed, Rust emerged as the most timely and persuasive solution.

2.2 Innovation in Developer Experience (DX): ‘Cargo’ and the Modern Toolchain

No matter how outstanding a language is, it cannot be widely adopted if it is difficult and inconvenient to use. A key element that cannot be omitted when discussing Rust’s success is the modern Developer Experience (DX) centered around its official build system and package manager, Cargo.

While the C/C++ ecosystem suffered for decades from fragmented build systems like Makefile, CMake, and autotools, and non-standardized dependency management issues, Rust provided a single, consistent toolchain from its inception. Developers can handle project creation, dependency management, building, testing, and documentation with just a few simple commands like cargo new, cargo build, and cargo test.

This was a revolutionary change that dramatically reduced friction in the development process. Just as npm fueled the explosive growth of the JavaScript ecosystem and pip did for Python, Cargo was the core infrastructure that drove the rapid growth of the Rust ecosystem. Despite the clear disadvantage of Rust’s steep learning curve, this powerful and convenient toolchain played a decisive role in why developers rate it as “highly productive.”

2.3 The Construction of a Powerful Narrative and Successful ‘Agenda-Setting’

The success of a technology is not determined solely by its technical superiority. The story surrounding the technology—its narrative—and how appealing and persuasive it is, shapes public perception. Rust executed a highly successful strategy in this regard.

  • Clear Value Proposition: Concise and powerful slogans like “fearless concurrency” and “safety without performance degradation” clearly communicated the problems Rust aimed to solve and its value.
  • Successful ‘Agenda-Setting’: The Rust discourse, through its confrontational framing with C/C++, elevated ‘memory safety’ as the most critical criterion for evaluating systems programming languages. By raising a value that was previously taken for granted to the center of the discussion, Rust succeeded in creating a competitive arena where it held the advantage. This can be analyzed as a successful case of ‘agenda-setting,’ where a technical community shaped public perception around a specific value and secured a leading role.

This powerful narrative provided developers with a clear motivation to learn and use Rust and acted as a focal point for forming a strong identity and sense of pride within the community.

2.4 Strategic Sponsorship and an Open Community Culture

Unlike many other languages led by individuals or small groups, Rust received sponsorship from a credible institution, Mozilla, from its early days. This provided confidence in the project’s stability and long-term development. This later led to the establishment of the Rust Foundation, with participation from companies like Google, Microsoft, and Amazon, further solidifying its position. Such strong institutional and corporate backing spread the perception that Rust was not just a hobby project but a serious endeavor to solve key industry problems.

Simultaneously, the Rust project officially adopted a Code of Conduct and emphasized an open and inclusive culture that welcomed new participants. In particular, systematic and friendly official documentation, such as “The Rust Programming Language” (commonly known as “The Book”), was highly praised by developers trying to learn complex concepts and contributed significantly to lowering the barrier to entry.

2.5 Conclusion: The Synergy of Success Drivers

In conclusion, Rust’s success is not the result of any single factor but a synergy of all the drivers analyzed above.

  1. It presented a clear technical solution to a core problem (‘safety without performance degradation’).
  2. It supported this with an innovative developer experience (Cargo).
  3. It propagated its value through a powerful and appealing narrative.
  4. It laid the foundation for a sustainable ecosystem through sponsorship from credible institutions and an open community.

Understanding these multifaceted drivers of success provides the necessary background to evaluate Rust’s technical limitations and discursive issues, discussed in other chapters of this book, from a more balanced perspective. Rust’s success is by no means an accident, and its success formula holds important lessons that other technologies and communities can learn from.

Part 2: A Technical Re-evaluation of Core Values

Part 1 examined the technical features that propelled Rust’s rise as a successful language and the narrative of its success. In Part 2, we will take a step further to technically re-evaluate Rust’s most central values: ‘safety’ and ‘ownership.’

We will analyze from multiple angles what engineering trade-offs exist behind the perception of these values as ‘innovations,’ and how these concepts have inherited and evolved from precedents in the history of programming languages like C++ and Ada. Through this, this part aims to establish a critical foundation for understanding Rust’s core design philosophy from a deeper and more balanced perspective.

3. A Multifaceted Analysis of the “Safety” Narrative

3.1 The Meaning of ‘Innovation’ and Analysis of Historical Precedents

Rust is evaluated as an ‘innovation’ for simultaneously pursuing the goals of ‘performance’ and ‘safety,’ presenting a new approach to the existing design methods of systems programming. To analyze the meaning of this ‘innovation’ from an engineering and historical perspective, it is necessary to examine the technical precedents on which Rust’s core concepts are based.

The advancement of software engineering is achieved through the succession of existing ideas and their new application. This section analyzes how Rust’s core concepts are connected to ideas developed in languages such as C++, Ada, and functional languages.

Specifically, this section references Ada and its subset SPARK as an important basis for comparison. This is because Ada/SPARK is a historical precedent that achieved Rust’s core goal of ‘safety without a GC’ decades ago in a different way. Therefore, comparing the two technologies can be an effective analytical tool for clearly understanding the points at which Rust’s approach possesses originality.

Ownership and Resource Management: Succession of the C++ RAII Pattern

Rust’s ‘ownership’ model can be understood as an extension of the resource management techniques developed in C++. C++ established the RAII (Resource Acquisition Is Initialization) design pattern, which links the lifecycle of a resource to the lifecycle of an object to automatically release resources when the destructor is called, and specified this through smart pointers.

The concept of managing memory through resource ‘ownership’ was first established in C++. Rust’s contribution lies in making this idea a mandatory rule enforced by the compiler across all areas of the language, rather than a pattern to be used selectively. This can be evaluated as a more systematic and comprehensive application of the existing concept. (A more detailed analysis of C++’s RAII and smart pointers follows in section 4.1.)

Safety without GC: The Precedent of Ada/SPARK

One of Rust’s main features is ‘memory safety without a garbage collector (GC).’ This goal was first pursued in the Ada language, developed in the 1980s under the leadership of the U.S. Department of Defense. Ada was designed for high-integrity systems and prevents errors such as null pointer access and buffer overflows without a GC through its type system and runtime checks.

SPARK, a subset of Ada, goes a step further by introducing formal verification.2 This is a technique for mathematically proving certain properties of a program (e.g., the absence of runtime errors), providing a different scope and level of reliability than the memory safety guarantees offered by Rust’s borrow checker. (A more detailed comparison follows in section 3.4.)

Of course, Rust’s borrow checker has the practical difference of approaching memory safety issues in a more automated way than formal verification. However, a historical precedent exists where the goal of ‘achieving safety without a GC’ was first implemented in the Ada/SPARK ecosystem.

Explicit Error Handling: The Influence of Functional Programming

Rust’s explicit error handling method through Result and Option also has its roots in existing programming paradigms. It borrows from the ‘Algebraic Data Types (ADT)’ and monadic error handling techniques developed in ML-family functional languages like Haskell and OCaml. These languages have long used their type systems to explicitly represent states of ‘no value’ or ‘error occurred’ and to enforce the handling of all cases by the compiler.

Integration and Enforcement of Concepts

As seen, Rust’s core concepts did not arise independently but are the result of integrating ideas from existing languages. Examples include the RAII principles of C++, the pursuit of safety without a GC from Ada/SPARK, and the type-based error handling from functional languages.

Therefore, Rust’s design features can be analyzed as an attempt to integrate multiple concepts into a single language and to provide safety guarantees across a wide range of code by ‘enforcing’ them as the language’s default rules through the compiler.

3.2 Practical Innovation: The Democratization of Value and Its Discursive Function

The preceding section (3.1) analyzed how Rust’s core concepts are deeply rooted in preceding technologies like C++ and Ada. In response to this analysis, proponents of Rust’s value often argue that the essence of its innovation lies not in ‘conceptual invention’ but in the ‘democratization of value.’

The core logic of this argument is as follows: The high level of safety pursued by Ada/SPARK required a high cost (a steep learning curve, complex specialized tools, slow development speed, etc.) that could only be afforded in a few specialized fields like aviation and defense. Consequently, this value had no practical meaning for the vast majority of general developers. In contrast, Rust, through an excellent Developer Experience via the modern build system Cargo, relatively accessible learning materials, and a vibrant community, successfully disseminated the value of ‘memory safety without a GC’—previously accessible only to a few experts—into the realm of general systems programming. In other words, the argument is that a ‘good enough’ technology that can be used by the many is a greater innovation in an engineering and practical sense than a theoretical perfection that can be used by the few.

This perspective accurately captures the significant contributions Rust has made to the software engineering ecosystem. Rust’s merits in improving the developer experience and raising awareness of the importance of memory safety are clear and should be highly praised in their own right.

However, the point this book focuses on is the way this argument of ‘practical innovation’ functions within the technical discourse. While the argument itself has validity, it is sometimes observed to function as a rhetorical tool to evade the critical question of ‘the absence of conceptual originality.’ Responding to the question, “Is A conceptually new?” with “A is successful in the market and easy to use,” is not a direct answer to the former question, even if the latter statement is true. This can amount to a shifting of the goalposts, changing the scope of the discussion from ‘conceptual origin’ to ‘practical utility.’

This logical shift can lead to a discourse that implies ‘conceptual uniqueness’ based on Rust’s ‘practical success.’ As a result, it can have the effect of unjustly devaluing or excluding from discussion the historical and engineering achievements that languages like Ada and C++ have built over decades.

In conclusion, the concept of ‘practical innovation’ has a dual nature: it explains Rust’s significant achievements while also functioning as a discursive mechanism to evade a critical examination of the original meaning of the term ‘innovation.’ This is an important case study showing how the meaning of a term can be redefined and the focus of a discussion strategically shifted in the process of emphasizing the excellence of a particular technology.

3.3 Defining ‘Memory Safety’ and Analyzing the ‘Memory Leak’ Problem

The value of ‘memory safety,’ which forms the core of the Rust discourse, can be analyzed more precisely when its scope and definition are clarified. While the memory safety guarantees provided by Rust are considered a significant achievement in the field of systems programming, this term does not encompass all types of memory-related issues.

The Rust compiler statically prevents fatal memory errors that can cause Undefined Behavior (UB), such as null pointer dereferencing, use-after-free, and data races, at compile time. This is a clear technical advantage that Rust has over C/C++.

However, there is a notable exception not covered by Rust’s safety guarantees: the memory leak. A memory leak, where a program fails to free allocated memory, causing the system’s available memory to gradually decrease, can lead to serious problems in long-running applications like servers. A classic example that causes a memory leak is a reference cycle created using shared pointers like Rc<T> and RefCell<T>, which is classified as ‘safe’ Rust code.

This technical limitation itself is a known issue in many languages that use reference counting. The point this book aims to highlight is the structure of the discourse regarding how the term ‘memory safety’ in Rust is defined and communicated.

According to Rust’s official documentation, The Rustonomicon, the guarantee of ‘safety’ in Rust is that “safe code will never cause Undefined Behavior (UB).”3 According to this definition, a memory leak is not UB that makes the program’s behavior unpredictable, and therefore it is not included in the scope of Rust’s safety guarantee. This is a technically clear definition.

The problem arises from the gap between this strict, technical ‘definition’ and the general ‘perception’ that developers expect from the term ‘memory safety.’ The term ‘memory safety’ can often be interpreted in a comprehensive sense, as if ‘all kinds of memory-related problems have been solved.’ This discrepancy in perception can create the effect that Rust has solved all memory problems for those who are not precisely aware of the specific scope of Rust’s safety guarantees.

This method of discourse formation shows a marked difference from the culture of other language communities. In the C language community, for example, it is explicitly shared that memory management is entirely the developer’s ‘responsibility.’ All memory-related discussions, including memory leaks, are actively conducted as ‘technical challenges’ to be solved.

In contrast, in some online technical discussions, a tendency is observed to evade discussions on certain issues like memory leaks by citing the ‘scope of the tool’s guarantee’ as being ‘off-topic.’ This can be seen as an approach that separates the responsibility for problem-solving by labeling it as ‘outside the tool’s guarantee,’ rather than internalizing it as a matter of ‘developer competence.’ This difference in approach highlights not only the design philosophies of each language but also a significant point about how communities perceive and discuss technical limitations. This gap between technical definition and popular perception becomes the backdrop for a defensive mechanism to operate when criticism of a specific problem (e.g., memory leaks) is raised, framing the problem itself as ‘off-topic’ and evading discussion (see Section 8.4, Case Study 2).

3.4 Levels of Assurance: The Mathematical Proof of Ada/SPARK and Rust’s Limits

It is clear that Rust’s safety model represents a significant step forward compared to C/C++. However, to objectively evaluate its level of assurance, it is necessary to understand the value of ‘safety’ on a broader spectrum. As stated in the preface, this section will use Ada/SPARK as an ‘analytical tool’ to clarify where Rust’s safety model is positioned on the safety assurance spectrum of systems programming languages as a whole. That is, by comparing it with the ‘mathematically proven correctness’ provided by SPARK, we will explore the engineering value and limitations of Rust’s ‘practical safety’ model. This comparison is not intended to determine the superiority of any particular language or to discuss realistic alternatives, but to clearly understand the trade-offs chosen by different design philosophies.

Rust’s Safety Assurance: Preventing ‘Undefined Behavior (UB)’

Rust’s core safety assurance is the prevention of memory access errors and data races that cause Undefined Behavior (UB) at compile time through its ‘ownership’ and ‘borrowing’ rules. This means that once a program compiles, these types of bugs will, in principle, not occur, and this is considered a significant engineering achievement as it is accomplished without runtime performance degradation under the ‘zero-cost abstraction’ principle.

However, Rust’s compile-time guarantees are focused on this area. It does not guarantee the overall logical correctness of the program or the absence of all kinds of runtime errors. For example, errors such as integer overflow and array index out of bounds can still occur, leading not to an unpredictable state, but to a controlled program termination called a ‘panic.’ This is a different level of issue from ensuring the stable ‘execution’ of a system.

Ada/SPARK’s Safety Assurance: Proving ‘Program Correctness’

In contrast, the Ada/SPARK ecosystem targets a broader range of correctness.

  1. Ada’s Default Safety and Resilience: At the language level, Ada attempts to prevent logical errors through its type system and ‘Design by Contract.’ In particular, it is designed with ‘resilience’ in mind, with the default behavior being to raise an exception upon various runtime errors, including integer overflow. This is not simply about terminating the program, but about allowing the system to continue its mission through error handling routines. This shows a fundamental difference in goals from Rust’s panic philosophy, which treats errors as ‘unrecoverable’ and terminates the thread.

  2. SPARK’s Mathematical Proof: SPARK, a subset of Ada, goes a step further by using formal verification tools to mathematically analyze the logical properties of the code. This makes it possible to ‘prove’ at compile time that runtime errors (including integer overflow, array index out of bounds, etc.) will not occur at all.

Comparison of Assurance Levels between the Two Languages

Error Type Rust Ada (Default) SPARK
Memory Errors (UB) Blocked at compile time (Guaranteed) Blocked at compile/run time (Guaranteed) Absence mathematically proven
Data Races Blocked at compile time (Guaranteed) Blocked at compile/run time (Guaranteed) Absence mathematically proven
Integer Overflow panic (unrecoverable halt) or wrap (config-dependent) Runtime exception raised (recoverable) Absence mathematically proven
Array Out of Bounds panic (unrecoverable halt) Runtime exception raised (recoverable) Absence mathematically proven
Logical Errors Programmer’s responsibility Partially prevented by Design by Contract Absence can be proven per contract

Conclusion: Rust’s Position on the Safety Spectrum

In conclusion, this comparative analysis, while acknowledging that Rust’s safety is a significant advance, shows that it is not the only or final point on the ‘safety’ spectrum. While SPARK demands a high cost in terms of explicit developer proof effort and the use of specialized tools for the ‘highest level of assurance,’ Rust can be seen as having chosen the cost of a developer’s learning curve and limited the scope of some guarantees for ‘universal and automated safety.’ In other words, the two technologies target different markets and development environments and should be understood not as being in a direct competitive relationship, but as presenting different solutions to different engineering problems.

Therefore, a discourse that uses only C/C++ as a point of comparison when evaluating Rust’s safety may have limitations in accurately grasping Rust’s technical position in the entire history of systems programming. For a more mature understanding, a multifaceted comparison with various technical alternatives is essential.

3.5 The Reality of the Comparison: The Multi-layered Safety Net of an Evolving C++

The discourse that emphasizes Rust’s safety often proves its value through comparison with C/C++. In this process, C/C++ is often regarded as a ‘language of the past’ that has failed to solve memory problems. However, for such a comparison to be valid, the subject of comparison should not be the C/C++ of the 1990s, but the ‘modern C/C++ ecosystem’ that has undergone numerous advancements.

Over the past two decades, the C++ language and its ecosystem have built a multi-layered approach to ensuring safety. However, this safety net has a fundamental difference from Rust’s compiler-integrated guarantees in that it requires conscious choices by the developer, additional costs, and strict discipline.

1. Language Evolution: The ‘Optional’ Safety of Modern C++ and Smart Pointers

First is the evolution of the language itself. Since the C++11 standard, ‘Modern C++’ has introduced smart pointers (std::unique_ptr, std::shared_ptr) into its standard library, actively supporting the RAII pattern at the language level. This is an effective way to prevent many of the chronic memory-related problems of past C++ at the language level by clarifying resource ownership and managing memory automatically.

However, the use of smart pointers in C++ remains a ‘best practice,’ not a mandatory one. A developer can always use raw pointers, and the compiler will not prevent it. This means that the final responsibility for safety still depends on the developer’s discipline, and mistakes can still occur.

2. Ecosystem Maturity: A Multi-layered Defense Demanding ‘Cost and Expertise’

Second is the support of a mature tool ecosystem that encompasses both static and dynamic analysis. Today’s professional C/C++ development environments can ensure safety by utilizing automated tools such as:

  • Static Analysis: Tools like the Clang Static Analyzer, Coverity, and PVS-Studio, which precisely analyze the entire codebase before compilation to find potential bugs, are widely used.
  • Dynamic Analysis: Tools like Valgrind and address sanitizers, which monitor memory access during program execution to detect subtle memory errors at runtime that are difficult to catch with static analysis alone, serve as an important safety net.
  • Real-time Linting: Linters like Clang-Tidy provide real-time feedback by pointing out potential errors as the developer writes code. In particular, they enforce many rules from the C++ Core Guidelines4 to encourage a safer coding style.

While these powerful tools greatly enhance safety, powerful commercial tools are often expensive, and correctly setting them up and interpreting the analysis results requires considerable expertise. This is a fundamental difference in accessibility and universality from the static analysis features provided by default and at no extra cost in Rust’s official toolchain (cargo).

3. The Approach of Mission-Critical Systems: The Strict Discipline of ‘Specialized Fields’

Third, in ‘mission-critical’ systems fields like automotive, aviation, and medical devices, where extreme reliability is required, much stricter methodologies are applied.

  1. Enforcement of Coding Standards: The use of dangerous language features is fundamentally banned through coding standards like MISRA C, MISRA C++.
  2. Specification of Code Contracts: Explicit ‘contracts’ are added to the code using annotation languages like SAL or ACSL.
  3. Static Verification: The possibility of runtime errors is mathematically verified using static code verification tools like Polyspace or Frama-C.
  4. Compiler Validation: Safety standards like DO-178C (aviation) or ISO 26262 (automotive) require a process to prove that the compiler has correctly translated the source code into machine code. This is achieved through ‘Qualification Kits’ provided by specialized vendors in the Ada or C/C++ ecosystems, which is possible because of the language’s standardization and mature commercial ecosystem. In contrast, Rust has the practical limitation that its tool and vendor ecosystem for supporting such official safety standard certifications (e.g., ISO 26262) is not yet as mature as that of C/C++ (see Section 7.2).

These approaches prove that C/C++ code can be made safe to a very high level. However, this is limited to extremely specialized fields and involves enormous costs and efforts that significantly hinder development productivity, making it unsuitable for general software development.

Conclusion: The Difference Between ‘Optional Effort’ and ‘Enforced Default’

It is an undeniable fact that the modern C++ ecosystem has developed sophisticated and multi-layered safety methodologies to solve its own problems. Furthermore, this development is not just a defensive response to existing problems but is leading to a fundamental ‘evolution’ of the language itself. For example, std::expected, introduced in the C++23 standard, is an attempt to explicitly handle error values as part of the type system, much like Rust’s Result type, which is an important example of the positive exchange of ideas between programming paradigms.

However, it is precisely at this point that the fundamental approach of C++ and the value of Rust are clearly distinguished. In C++, using safe features like std::expected is a ‘best practice’ that still depends on the developer’s ‘choice.’ This means it requires optional effort at the language level, in addition to expensive external tools or strict discipline. In reality, these latest standards and methodologies are not consistently applied in the majority of projects, and for that very reason, memory-related security incidents continue to occur constantly.5

In conclusion, Rust’s compiler-integrated safety features, when compared to C++’s multi-layered safety net, show a fundamental difference in that they have shifted the value of ‘safety’ from an optional effort of a few experts to an ‘enforced default’ for all developers. Separately from respecting modern C++’s safety assurance methods, one must understand that the ‘reality’ that these methods are not universally applied is precisely why an alternative like Rust gains such strong persuasive power.

3.6 Alternative Memory Management: A Re-evaluation of Modern Garbage Collection

When evaluating Rust’s memory management approach, the most frequently compared target is the manual management of C/C++. However, in the broad spectrum of systems programming, languages that achieve both memory safety and high productivity through a garbage collector (GC) (e.g., Go, C#, Java) also hold a significant position.

In some parts of the Rust discourse, GC-based languages are sometimes argued to be unsuitable for certain systems programming domains based on the unpredictable ‘Stop-the-World’ pauses and runtime overhead of GCs. While this argument may have had validity in the past when GC technology was relatively immature, it may not adequately reflect the characteristics of modern GCs that have advanced dramatically over the past two decades.

The GCs implemented in today’s mainstream languages manage memory while minimizing application pauses through sophisticated techniques such as generational GC, concurrent GC, and parallel GC. For example, the Go language’s GC is designed with a very short pause time target in the microsecond (µs) range, making it the foundation for numerous high-performance network servers and cloud infrastructure. The latest GCs in the Java world, such as ZGC and Shenandoah GC, aim for millisecond (ms) pauses even with heaps of several hundred gigabytes (GB) (see: relevant official documentation), challenging the old notion that ‘GC stops the system.’

Ultimately, it is more accurate to understand Rust’s ownership model and modern GCs not as a matter of absolute superiority, but as a difference in design philosophy regarding ‘how to pay the cost.’

  • Rust’s Approach: Minimizes runtime costs but transfers that cost to compile time and the developer’s learning curve, i.e., to ‘developer time.’ The developer must invest cognitive effort to learn and adhere to the ownership and lifetime rules.
  • Modern GC Languages’ Approach: Reduces the developer’s cognitive load and development time but uses a small amount of runtime CPU and memory resources, i.e., ‘machine time,’ as the cost.

Of course, there are certainly domains where the presence of a GC itself is a burden, such as in extremely resource-constrained embedded systems or hard real-time operating systems. However, generalizing these specific requirements to devalue the practicality of all GC-based languages may be to overlook the needs of various business environments. In many commercial settings, development speed and time-to-market are more important than raw runtime performance, and in these cases, GC languages that maximize development productivity are a very rational and economical choice.

3.7 The unsafe Paradox: C ABI Dependency and the Boundary of Guaranteed Safety

Rust’s compile-time safety guarantees are only valid within the domain classified as ‘safe’ code. However, Rust provides an explicit path to intentionally bypass the compiler’s strict rules through the unsafe keyword. The existence of unsafe is not merely an exception feature but a core concept that shows the extent of Rust’s safety guarantees and how that boundary connects to the outside world.

The necessity of this unsafe keyword is most fundamentally enforced by the reality of interoperating with external languages, i.e., FFI (Foreign Function Interface). All major modern operating systems, hardware drivers, and decades of accumulated core libraries use the C language’s ABI (Application Binary Interface) as a de facto standard interface. For a Rust program to perform any meaningful task—such as reading a file, communicating over a network, or drawing something on the screen—it must ultimately call an operating system API implemented with the C ABI.

It is precisely at this point that a fundamental irony of Rust arises. Rust presents itself as a ‘replacement’ to solve the memory problems of C/C++, yet to perform its functions, it must structurally depend on the C ABI, the foundation of the C/C++ ecosystem. Using FFI essentially means disabling Rust’s safety net and accepting C’s memory model, and this process inevitably requires an unsafe block. This is because the Rust compiler cannot verify whether the C code beyond the FFI boundary will keep its promises (e.g., a pointer is never null, a buffer is large enough).

Consequently, the very dangers that Rust sought to solve (null pointers, buffer overflows, etc.) can be re-introduced into a Rust program through the ‘unsafe boundary’ of FFI. This shows the structural limitation of why the slogan of some, “Rewrite It In Rust (RIIR),” is difficult to realize in practice.

Of course, unsafe is used for other purposes besides FFI.

  • Low-level hardware and OS interaction: Direct control of hardware registers not through the C ABI, etc.
  • Overcoming the limitations of the compiler’s analysis model: Even core data structures in the standard library, like Vec<T>, internally use unsafe code for highly optimized memory management that the borrow checker cannot prove.

In particular, the pattern of using unsafe code internally to provide a ‘safe’ interface to the user is an important and widely used pattern throughout the Rust ecosystem. But the very structure of this pattern leaves an important question. If there is a bug in the unsafe implementation inside that ‘safe’ interface, where does the responsibility lie? The answer to this question becomes clearer when comparing how responsibility is attributed differently from the culture of the C/C++ ecosystem.

In the C/C++ ecosystem, a memory bug in a library tends to be accepted as a manifestation of a widely known, inherent risk in the language itself. Therefore, discussions about the bug are primarily focused on the technical cause and solution of the bug itself, and it is often perceived as a case that reaffirms the fundamental ‘unsafety’ of the language. The responsibility lies with the developer who created the bug, but the failure is understood within the larger framework of the language’s inherent risks.

In contrast, Rust has a strong and explicit linguistic promise that ‘Safe Rust guarantees memory safety’ as its core identity. Because of this, when a memory error occurs in unsafe code, the discussion may tend not only to criticize the individual developer who wrote the bug but also to perform a discursive function of defending the core narrative that ‘the safety guarantee of Safe Rust has not been compromised.’ In other words, the cause of the failure is clearly separated and attributed not as a ‘failure of the system guaranteed by the compiler,’ but as a ‘human failure in the unsafe domain that the developer must guarantee.’

This logical structure, which simultaneously maintains the integrity of Safe Rust while attributing the responsibility for failure to the individual writer of the unsafe code, can be considered a unique point that shows how Rust’s safety model is perceived and defended.

3.8 The Meaning of “Safe Failure”: The Relationship Between panic and System Robustness

One of the claims often mentioned when discussing Rust’s safety model is that “Rust fails safely, even when it fails.” To analyze the meaning of this claim precisely, we first need to distinguish the term ‘failure’ from two different perspectives.

  • ‘Safe failure’ from a memory integrity perspective: This refers to a failure that does not cause Undefined Behavior (UB) or data corruption, but terminates the program in a controlled manner.

  • ‘Unrecoverable halt’ from a service continuity perspective: This refers to a state where, upon an error, it is impossible to recover the logic or continue the service through exception handling, and the corresponding thread or process terminates.

Rust’s panic is clearly a ‘safe failure’ from the former perspective, but from the latter perspective, it corresponds to an ‘unrecoverable halt.’6 This section aims to analyze the relationship between this dual nature of panic and the overall robustness and resilience of a system.

The Technical Difference and User Perspective Between panic and Segmentation Fault

Technically, a panic is clearly different from a segmentation fault. While a segmentation fault can lead to memory corruption or unpredictable secondary damage, Rust’s panic by default safely unwinds the stack, calls the destructor (drop) of each object, and terminates the program in a controlled manner. This process preserves data integrity and facilitates debugging, offering clear engineering advantages.

However, when the perspective shifts from the ‘developer’ to the ‘end-user,’ this technical elegance takes on a different meaning. To the user, the abrupt termination of a program is essentially the same ‘service failure,’ regardless of whether the cause is a controlled panic or an unpredictable crash. Therefore, interpreting the technical advantages of panic as the final victory of ‘safety’ may cause one to overlook another important value: the ‘continuous survival of the system’ or ‘service resilience.’

The Impact of “Safe Failure” on Development Culture

Furthermore, the fact that a ‘safe failure’ is guaranteed can have a paradoxical effect on development culture.

In a C/C++ development environment, the possibility that a memory error could lead to an unpredictable disaster often emphasizes a defensive programming culture that tries to guard against potential errors and handle all exceptional situations.

In contrast, in Rust, the existence of panic, which ensures that “in the worst case, the program terminates safely without data corruption,” can encourage developers to explicitly cause a panic using .unwrap() or .expect() rather than delicately handling all errors with the Result type. This can lead to a development style that opts for ‘termination’ when a problem occurs, instead of making an effort to ‘recover’ the system from a complex error, which has been a point of criticism.

In conclusion, panic is a failure handling mechanism with clear advantages in terms of preserving data integrity and ease of debugging. However, it is necessary to critically examine the point that the concept of ‘safe failure’ may work in a way that weakens the effort to design for the overall ‘robustness’ of a program that tries to recover from errors and continue service. This ultimately raises an important question about how the characteristics of a tool affect the design philosophy and culture of developers.

3.9 The Scope and Limits of the “Safety” Guarantee

Having examined Rust’s safety model from various angles, it is finally necessary to clarify the scope of its guarantee. As ‘safety’ is repeatedly emphasized in the Rust discourse, there is a possibility that this term is sometimes over-interpreted as ‘safety from all kinds of bugs.’ However, the guarantee provided by the Rust compiler is focused on the specific area of ‘memory safety.’

Other major classes of bugs that the compiler does not detect, and therefore remain entirely the developer’s responsibility, are as follows:

  • Logical errors This is the most common type of error in software. It occurs when the program’s logic itself is flawed, for example, applying a discount rate twice or using the wrong interest rate in a financial calculation. Rust’s type system and borrow checker validate the validity of memory access, but they do not verify whether the business logic of the code is ‘correct as intended.’

  • Integer overflow While debug builds will panic on integer overflow, release builds, where performance is prioritized, default to wrapping the value. This is an intended design specification (see: The Rust Book, “Integer Overflow”), but if not explicitly handled by the developer, it can become a source of logical bugs leading to unexpected data corruption or calculation errors.

  • Resource Exhaustion

    • Memory leak: As analyzed in Section 3.3, a reference cycle using Rc<T> and RefCell<T> can cause a memory leak, despite being classified as ‘safe’ code.
    • Other resources: This is a problem that occurs when limited system resources like file handlers, network sockets, or database connections are not released after use. Rust’s RAII pattern (Drop trait) assists in resource deallocation, but this is only when the developer has correctly implemented Drop for the relevant type, and the language does not automatically guarantee the management of all kinds of resources.

  • Deadlock Rust’s ownership system effectively prevents ‘data races,’ which occur when multiple threads try to write to the same data simultaneously. However, it does not prevent ‘deadlocks,’ where two or more threads hold different resources (e.g., mutexes) and wait indefinitely for each other’s resources. This is not a memory safety issue, but a logical problem in concurrency design.

In short, it is a significant engineering achievement that Rust effectively prevents the chronic memory error classes of C/C++ at compile time. Nevertheless, this does not guarantee ‘bug-free software.’ The responsibility for ensuring the overall correctness and reliability of software ultimately rests on the developer’s design capabilities and rigorous testing, regardless of the tool used.

3.10 Performance, Safety, and Productivity: The Trade-offs of Programming Language Design

In software engineering, a guiding principle is that a single tool can rarely satisfy all requirements, and this applies to programming language design. Engineering design is generally a process of balancing trade-offs between multiple objectives.

The design direction of a programming language is typically determined by three main axes: performance and memory control, developer productivity, and compiler-level safety. Each language and its ecosystem selects a specific point among these three values, resulting in different advantages and costs.

  • C/C++: Prioritizes hardware control and execution performance. To achieve this, developers must take on more responsibility, including manual memory management.
  • Go, Java/C#: Focuses on increasing developer productivity through a garbage collector (GC) and a runtime. This design can align with the requirements of web services and enterprise application environments.
  • Rust: Aims to achieve both C++-level performance and memory safety simultaneously, without a GC. This requires developers to learn and apply the ownership and borrow checker model in their code.

Due to these design differences, each language may demonstrate different suitability in specific development scenarios.

  • Web Service Backend Development: In this field, fast development speed and time-to-market can be critical factors. Go’s concurrency model or the C#/.NET enterprise ecosystem provides the high productivity to meet these requirements.
  • High-Integrity Systems: In environments where runtime errors must be minimized, such as flight control systems, mathematically provable stability may be required. Ada/SPARK achieves this goal through formal verification, which necessitates additional development cost and effort.
  • Network Proxies and CLI Tools: In domains where memory safety and high performance are simultaneously required, and the overhead of a GC is a concern, Rust’s design goals can be an effective fit.

In conclusion, every language has specific advantages and associated costs according to its unique design goals. Therefore, the engineering approach is to analyze the constraints and requirements of a specific problem domain to select the most appropriate tool.

4. Re-evaluating the “Ownership” Model and Its Design Philosophy

4.1 The Origins of the Ownership Concept: C++’s RAII Pattern and Smart Pointers

To understand Rust’s core feature, the ownership model, one must first examine the historical context in which the concept was born, particularly how resource management evolved in the C/C++ languages.

C’s Manual Memory Management and Its Limitations

The C language grants programmers direct control over dynamic memory through the malloc() and free() functions. While this design provides a high level of flexibility and performance, it places the entire responsibility on the programmer to free all allocated memory at the correct time, and exactly once.

This manual management model can lead to the following chronic memory errors if a programmer makes a mistake:

  • Memory leak: Available memory gradually decreases because allocated memory is not freed.
  • Double free: Freeing already freed memory, which corrupts the state of the memory manager.
  • Use-after-free: Accessing a freed memory region, which can lead to data corruption or security vulnerabilities.

These problems revealed the inherent limitations of a system that relies solely on the programmer’s personal responsibility, leading to the search for a new paradigm in C++ to solve this systematically.

The Evolution of C++: The RAII Pattern and Smart Pointers

C++ introduced the RAII (Resource Acquisition Is Initialization) pattern to shift the responsibility of resource management from the individual programmer to the language’s object lifecycle management rules. RAII is a technique where a resource is acquired in an object’s constructor and automatically released in its destructor. Since the C++ compiler guarantees that the destructor will be called when an object goes out of scope (including during normal termination and exception handling), it can prevent resource release omissions due to programmer error at the source.

The most representative example of applying the RAII pattern to dynamic memory management is smart pointers. Smart pointers introduced since the C++11 standard, in particular, show philosophical similarities to Rust’s ownership model.

  • std::unique_ptr (Unique Ownership): Represents exclusive ownership of a particular resource. The concept that copying is forbidden and only ‘moving’ of ownership is allowed is directly linked to Rust’s default ownership model and move semantics.
  • std::shared_ptr (Shared Ownership): Provides a way for multiple pointers to safely co-own a single resource through reference counting. This is the foundational concept for Rust’s Rc<T> and Arc<T>.

Thus, C++ established the concept of ‘resource ownership’ through RAII and smart pointers and presented a systematic solution for handling it.

4.2 Rust’s Originality: Not ‘Invention of a Concept’ but ‘Compiler Enforcement’

The previous section confirmed that Rust’s ownership concept is deeply rooted in C++’s RAII pattern and smart pointers. This raises the question of where Rust’s originality lies. In conclusion, Rust’s engineering contribution is not the ‘invention’ of the concept itself, but the ‘manner of enforcement’ of the existing ownership principle at the language level by the compiler.

The Shift from Optional Pattern to Mandatory Rule

In C++, the use of smart pointers like std::unique_ptr is an effective design pattern for enhancing memory safety, but it is an ‘option’ for the developer. A developer can always choose not to follow this pattern and use raw pointers, and the compiler will not prevent it. This means the final responsibility for ensuring safety relies on the developer’s discipline and conventions.

In contrast, Rust has made the ownership rule not an optional pattern but a mandatory rule built into the language’s type system. Every value is governed by this rule, and a static analysis tool called the borrow checker verifies compliance at compile time. Unless an unsafe block is used, a violation of the rule is not just a warning but a compile error, preventing the program from being built in the first place.

This design shows a fundamental difference from C++ in that it shifts the agent of safety assurance from ‘developer discipline’ to ‘compiler static analysis,’ forming a core feature of Rust.

The Trade-off from the Perspective of a Skilled Developer

This characteristic of ‘compiler enforcement’ has a dual nature of utility and constraint from the perspective of a skilled C/C++ developer.

Skilled C/C++ developers can easily recognize that Rust’s ownership rules align with the best practices they have followed to prevent mistakes.

  • Rust’s move semantics are similar to the ownership transfer pattern using std::unique_ptr and std::move in C++.
  • Rust’s immutable (&T) and mutable (&mut T) references share a context with the design principles in C++ of using const T& to guarantee data immutability or to prevent concurrent modification.

From this perspective, Rust can be evaluated as a useful tool that explicitly enforces the ‘implicit discipline’ of the past through the compiler.

However, it is precisely this strict enforcement that can act as a limitation. When implementing complex data structures or performing extreme performance optimizations, a skilled developer can employ safe memory management patterns that are beyond the analytical capabilities of the borrow checker. Since the borrow checker cannot prove all valid programs, a situation can arise where code that is logically safe from the developer’s point of view is rejected simply because ‘the compiler cannot prove it.’

In conclusion, Rust’s ownership model is a significant engineering achievement that dramatically improves the average safety level of code through universal rule enforcement. At the same time, due to its design philosophy that prioritizes fixed rules over expert judgment, it contains a trade-off that can constrain development flexibility in certain situations.

4.3 Design Philosophy Comparison: Ownership Model vs. Design by Contract

Programming languages adopt different design philosophies to ensure correctness. The ownership and borrowing model used by Rust focuses on automatically preventing certain types of errors at compile time. In contrast, design by contract, utilized in languages like Ada/SPARK, uses a method where a tool verifies logical ‘contracts’ specified by the developer.

To analyze the differences between these two philosophies and their respective engineering trade-offs, we will use the implementation of a doubly-linked list, a fundamental data structure in computer science, as a case study.

1. Approach 1: Rust’s Ownership Model

A doubly-linked list has a structure where each node mutually references the previous and next nodes. This structure, which is relatively straightforward to implement using pointers or references in other languages, directly conflicts with Rust’s basic rules. Rust’s ownership system, by default, does not allow reference cycles or multiple mutable references to a single piece of data.

Therefore, the most intuitive form of a node definition is treated as a compile error by the borrow checker.

// Intuitive code that does not compile
struct Node<'a> {
    value: i32,
    prev: Option<&'a Node<'a>>,
    next: Option<&'a Node<'a>>,
}

To resolve this constraint within ‘safe’ Rust code, one must use the explicit ‘escape hatches’ provided by the language. This means using a combination of Rc<T> for shared ownership, RefCell<T> for interior mutability, and Weak<T> to break reference cycles.

// Example implementation using Rc, RefCell, and Weak
use std::rc::{Rc, Weak};
use std::cell::RefCell;

type Link<T> = Option<Rc<Node<T>>>;

struct Node<T> {
    value: T,
    next: RefCell<Link<T>>,
    prev: RefCell<Option<Weak<Node<T>>>>,
}
  • Analysis: This approach offers the powerful advantage of the compiler automatically preventing certain types of concurrency issues, such as data races. The ownership rules enforce the safest state by default, and for cases requiring complex shared state, like a doubly-linked list, it guides the developer to explicitly opt-in to complexity using Rc, RefCell, etc. The cognitive cost and verbosity of the code incurred in this process can be considered the main cost of this design philosophy. The developer’s focus may shift more towards satisfying the compiler’s rules than on the logical structure of the problem.

2. Approach 2: Ada/SPARK’s Pointers and Design by Contract

Ada supports the use of pointers similar to C/C++ through its access type, allowing for a more direct representation of the doubly-linked list structure.

-- Intuitive representation using Ada
type Node;
type Node_Access is access all Node;
type Node is record
  value : Integer;
  prev  : Node_Access;
  next  : Node_Access;
end record;

By default, Ada ensures safety by checking for errors like null access dereferencing at runtime and raising a Constraint_Error exception.

Taking this a step further, SPARK, a subset of Ada, provides a way to mathematically prove the absence of runtime errors at compile time through design by contract. The developer specifies preconditions (Pre) and postconditions (Post) for procedures or functions, and a static analysis tool verifies whether the code always satisfies these contracts.

-- Example of safety proof through a SPARK contract
procedure Process_Node (Item : in Node_Access)
  with Pre => Item /= null; -- Specifies the contract 'Item is not null'
  • Analysis: This approach provides the flexibility for developers to represent data structures more directly through a pointer model similar to C/C++. Safety is ensured through runtime checks or through explicit contracts written by the developer and proofs by a static analysis tool. The cost of this design philosophy is the responsibility and effort required of the developer to consider all potential error paths and write them as formalized contracts. If a contract is missing or written incorrectly, the safety guarantee can be incomplete, which entails a different kind of risk than a system that relies on automated rules.

3. Design Philosophy Comparison and Conclusion

The two approaches allocate the responsibility and cost for ensuring software correctness to different agents and at different times.

Aspect Rust Ada/SPARK
Agent of Safety Compiler (Automatic enforcement of implicit rules) Developer + Tool (Writing explicit contracts and static proof)
Default Paradigm Restrictive by default, opt-in complexity Permissive by default, opt-in safety proof
Primary Cost High cognitive overhead and code complexity for certain patterns Burden of writing formal specifications for all interactions
Primary Benefit Automatic prevention of certain error classes like data races Direct expression of developer’s design intent and ability to prove a wide range of logical properties

In conclusion, it is difficult to evaluate Rust’s ownership model with a binary view of ‘innovation’ or ‘flaw.’ It is a unique design philosophy with distinct advantages and corresponding costs. While this philosophy is highly effective in preventing certain types of bugs, it contains a trade-off that requires developers to bear a high learning cost and use non-intuitive solutions for certain problems. The suitability of the language can be evaluated differently depending on the type of problem to be solved, the capabilities of the team, and the values prioritized by the project (e.g., automated safety guarantees vs. design flexibility).

Part 3: Ecosystem Realities and Structural Costs

Part 3 will analyze the realistic challenges faced by the Rust ecosystem and the structural costs behind them. When evaluating Rust’s developer experience (DX), the ‘zero-cost abstraction’ principle, and the constraints of real-world industrial application, it is important to understand that the problems we encounter can be divided into two categories:

  1. Problems of ‘maturity’: These are issues that can be naturally resolved or mitigated as time passes and the community’s efforts accumulate, such as a lack of libraries, instability of some tools, or insufficient documentation. These are maturity issues common to all growing technology ecosystems.

  2. Inherent ‘trade-offs’ in design: These are the result of intentionally sacrificing other values (e.g., ease of learning, compile speed, flexibility in implementing certain patterns) to achieve the language’s core values (e.g., runtime performance, memory safety without a GC). This is a matter of ‘choice,’ not a ‘flaw,’ and therefore is unlikely to disappear completely over time.

Based on this analytical framework, this chapter aims to clearly distinguish and evaluate which category of problem Rust’s various technical challenges fall into.

5. The Achievements and Costs of “Developer Experience” (DX)

5.1 The Borrow Checker, Learning Curve, and Productivity Trade-off

The core technology implementing Rust’s safety model is the borrow checker, which statically enforces rules of ownership, borrowing, and lifetimes at compile time. Due to its strictness, this mechanism forms a trade-off with developer productivity. Developers accustomed to other programming paradigms must restructure their existing approaches to apply Rust’s model, which leads to a learning curve.

The Duality of the Trade-off: Learning Costs and Safety Guarantees

The rules applied by the borrow checker incur cognitive costs during the development process, but at the same time, they provide the benefit of preventing certain types of runtime errors at their source.

  1. Costs and Benefits of the Ownership and Borrowing Model: Developers must apply a single-owner rule for every value and adhere to strict immutable or mutable borrow rules for data access. In this process, developers may invest additional effort to satisfy the compiler’s rules beyond implementing the core logic. However, through this cost, the compiler prevents concurrency issues like data races at compile time and eliminates the possibility of memory errors such as use-after-free.

  2. Costs and Benefits of Explicit Lifetimes: In cases where the compiler cannot automatically infer the validity of a reference, the developer must explicitly specify lifetime parameters ('a). This is a task that requires additional abstract thinking to pass the compiler’s static analysis. However, through this explicit notation, the compiler can statically verify and block the possibility of errors that reference invalid memory, such as dangling pointers.

  3. Constraints and Alternatives for Implementing Specific Design Patterns: The borrow checker’s analysis model makes it difficult to implement fundamental data structures like doubly-linked lists or graph structures requiring cyclical references using only the basic rules. This shows that there are limits to the range of programs that the borrow checker’s model can express. In such cases, developers can explicitly handle exceptions to the rules using Rc<T>, RefCell<T>, or unsafe blocks to implement the desired data structures.

Impact on Productivity and Related Discourse

These technical characteristics affect a project’s productivity. When a new member joins a development team, an adaptation period and training costs may be incurred (initial productivity decline), and feature implementation can be delayed by resolving compiler errors, which can lower the predictability of project schedules. This acts as a cost and risk in business environments where developer time is considered a resource.

This learning curve is part of a deliberate design trade-off chosen to achieve the goal of ‘safety without performance degradation.’ In some online discussions, a discourse is observed where this learning difficulty is reinterpreted as a measure of a developer’s skill enhancement or expertise. This perspective can lead to criticism that it frames the discussion of learning difficulties as an issue of individual capability, which may act as a barrier to new developers or limit discussions on improving the usability of the tools.

5.2 Tendency for Generalization in Technology Selection and Engineering Trade-offs

When a new technology emerges, a tendency to expand its application beyond its original purpose is often observed. This phenomenon, known as the “law of the instrument,” can be viewed as a common socio-psychological dynamic that appears during the technology adoption process.

The Rust language provides a case study for analyzing this phenomenon. The value of ‘memory safety’ offered by the language, and the learning time required to master it, lead developers to invest significant effort into the technology. This investment can, in turn, lead to attempts to expand its use beyond the specific areas where its strengths are most pronounced and into broader domains.

This section analyzes two aspects of how this ‘generalization’ tendency appears in discussions related to Rust. First, it examines the tendency for Rust’s key features (e.g., absence of a GC, runtime performance) to be used as exclusive evaluation criteria when assessing other programming languages. Second, using the example of general web application development, it explores how the analysis of engineering trade-offs, considering the specific nature and constraints of a problem, can be applied differently.

Bias in Comparison Methods with Other Technologies

The tendency for generalization in technology selection can be accompanied by a certain bias in how comparisons are made with other programming languages.

In some cases, Rust’s advantages—’memory safety without a garbage collector (GC)’ and ‘high runtime performance’—are applied as the primary criteria for evaluating technology. From this perspective, other languages may be assessed as follows:

  • C/C++: The absence of memory safety becomes the main basis for evaluation, more so than other aspects like its vast ecosystem or hardware control capabilities.
  • Go, Java, C#: The presence of a GC is analyzed as a potential cause of performance degradation, and the value of these languages’ developer productivity or mature ecosystems may be relatively understated.
  • Python, JavaScript: The lack of a static type system is presented as a basis for stability issues, and the features of these languages, such as rapid prototyping and development speed, may be considered secondary factors.

An engineering evaluation considers various trade-offs comprehensively. A method that selectively emphasizes only certain criteria can have limitations in objectively assessing the suitability of each technology for different problem domains.

Case Study: Generalization in Web Backend Development

One example of this generalization is the argument to use Rust for some web backend development.

Rust can be an effective choice in specific web service areas that require high performance and low latency, such as high-performance API gateways and real-time communication servers. Memory safety is also a factor that enhances server stability.

However, this argument can be seen as generalizing the requirements of specific areas where Rust has strengths to other web backend domains. In the development of many general web applications (e.g., SaaS, internal management systems, e-commerce platforms), the following business and engineering factors are also considered in addition to performance:

  • Development speed and time-to-market
  • Ecosystem maturity (completeness of libraries for authentication, payments, ORMs, etc.)
  • Ease of learning for new talent and the size of the developer talent pool

By these metrics, languages with established ecosystems like Go, C#/.NET, Java/Spring, and Python/Django may be more suitable choices. Arguing for the broad application of a specific technology without considering the nature of the problem and business constraints can be seen as an approach that does not sufficiently consider the analysis of engineering trade-offs.

5.3 The Complexity of the Asynchronous Programming Model and Its Engineering Trade-offs

Rust’s asynchronous programming model (async/await) is designed based on the ‘Zero-Cost Abstractions’ principle, aiming to achieve high runtime performance without a garbage collector or heavy green threads. This is an important design goal in the systems programming domain where operating system threads must be used efficiently.

However, this design choice entails a clear cost that the developer must bear: conceptual complexity and difficulty in debugging.

The Source of Technical Complexity

Rust’s async/await works by having the compiler transform asynchronous code into a complex state machine. This process can create ‘self-referential structs’ that contain references to themselves in memory, and Rust has introduced a special pointer type, Pin<T>, to guarantee the memory address stability of these structs.

Pin<T> and its related concepts like Generators are highly abstract concepts rarely found in other mainstream languages, requiring considerable study to understand how they work. This complexity can be seen as a form of ‘leaky abstraction,’ and even the core developers leading Rust’s async ecosystem have acknowledged the steep learning curve of these concepts and consistently raised the need for usability improvements in blogs and talks.7

Practical Impact on the Development Experience

The internal complexity of the async model causes the following difficulties in the actual development and maintenance process:

  1. Increased Difficulty in Debugging: The stack traces printed when an error occurs in async code are often composed of internal functions of the async runtime and obscure state machine calls generated by the compiler, making it difficult to trace the root cause of the error. Furthermore, unlike in synchronous code, local variables of an async function are captured inside the state machine object, making state tracking with a debugger very tricky.
  2. Cost Shifting: Consequently, Rust’s async model minimizes the runtime’s CPU and memory usage (machine time) but shifts that cost to the developer’s learning time and the difficulty of debugging (developer time), representing a design trade-off.

Comparative Analysis with Alternative Models

This trade-off becomes clearer when compared to an alternative asynchronous model like Go’s goroutines. Goroutines provide developers with a much simpler and more intuitive concurrency programming model through lightweight threads (green threads) managed by the language runtime.

Aspect Rust async/await Go Goroutine
Design Goal Zero runtime overhead Developer productivity and simplicity
Runtime Cost Minimized Slight cost due to scheduler, GC
Learning Curve High (requires concepts like Pin) Very low (go keyword)
Debugging Difficult (complex stack traces) Easier (clear stack traces)

Of course, the performance advantage of the Rust model may be clear in CPU-bound tasks. However, in a typical I/O-bound environment where network latency or database response time is the bottleneck, the complexity cost of development and debugging required by the Rust model may be felt more keenly than the slight runtime cost accepted by the Go model.

In some parts of the Rust community, a tendency is observed to devalue the Go model because it is not ‘zero-cost.’ However, this may be a biased view that evaluates technology solely on the single metric of ‘runtime performance’ and overlooks other important engineering values such as ‘developer productivity’ and ‘ease of maintenance.’

5.4 Reconsidering the Practicality of the Explicit Error Handling Model (Result<T, E>)

Rust has adopted an explicit error handling model that forces error handling at compile time through the Result<T, E> enum, pattern matching, and the ? operator. This model is valued as a powerful means of preventing the omission of error handling. This section will reconsider the practicality of this model from multiple angles by comparing it with alternative error handling methods, analyzing its historical origins, and examining the costs incurred in its actual use.

1. Comparison with Alternative Models: try-catch Exception Handling

When discussing Rust’s Result model, the try-catch-based exception handling model is often criticized for its unpredictable control flow. However, a mature exception handling mechanism has its own unique engineering values.

  • Separation of Concerns: By separating normal logic in a try block and exception handling in a catch block, code readability can be improved. Since control flow is immediately transferred from the point where an error occurs to the point where it is handled, the verbosity of manually propagating errors through multiple function levels (return Err(...)) can be avoided.
  • Compile-time Checking: The criticism that “you don’t know what exception will be thrown” does not apply in all cases. For example, Java’s ‘Checked Exceptions’ require that a function specify the exceptions it can throw in its signature, and the compiler enforces their handling. This is an example of achieving the goal of preventing error omission in a different way than the Result type.
  • System Resilience: Modern exception handling systems play an important role in preventing the abnormal termination of a program and continuing the stable operation of a service through error logging, resource deallocation (finally), and error recovery logic.

2. Historical Origins: Functional Programming

The explicit error and state handling approach using Result and Option is not a unique invention of Rust, but a successful adoption of a concept whose utility was proven long ago. The roots of this idea lie in the functional programming camp.

Types like Haskell’s Maybe a and Either a b, or the sum types of ML-family languages like OCaml and F#, have been using the type system to explicitly represent the absence of a value or an error state and forcing the compiler to handle all cases for decades.

Therefore, it is a more accurate assessment to say that Rust’s contribution is not in ‘inventing’ this concept, but in ‘reinterpreting’ it for the context of a systems programming language and ‘popularizing’ it through syntactic sugar like the ? operator.

3. Practical Cost: The Verbosity of Error Type Conversion

While the ? operator is very convenient in scenarios where the same error type is propagated, it reveals its limitations in real applications that use various external libraries. Each different library returns its own unique error type (e.g., std::io::Error, sqlx::Error), and the developer must repeatedly write boilerplate code to convert them into a single application error type.

// Example of converting several different kinds of errors into a single application error type
fn load_config_and_user(id: Uuid) -> Result<Config, MyAppError> {
    let file_content = fs::read_to_string("config.toml")
        .map_err(MyAppError::Io)?; // std::io::Error -> MyAppError

    let config: Config = toml::from_str(&file_content)
        .map_err(MyAppError::Toml)?; // toml::de::Error -> MyAppError

    // ...
    Ok(config)
}

To resolve this verbosity, external libraries like anyhow and thiserror are widely used. However, the very fact that the use of an external library is considered a de facto standard for a particular feature in the ecosystem (in this case, flexible error handling) is a point that suggests that there is an inconvenience in developing practical applications using only the basic features of the language.

5.5 Analysis of the Rust Ecosystem’s Qualitative Maturity and Community Discourse

Rust’s official package manager, Cargo, and its central repository, Crates.io, have played a pivotal role in the language’s rapid adoption and growth. This has led to a quantitative expansion, with a vast number of libraries, or crates, being shared. However, behind this quantitative growth lies the challenge of qualitative maturity, which is crucial for ensuring stability and reliability in production environments. This section analyzes the main qualitative challenges facing the Rust ecosystem and examines the characteristic discourse structure of the community in response to these issues.

Developers using Rust in production environments may face the following realistic problems related to the library ecosystem:

  • Lack of API Stability: A significant number of crates are maintained at a version below 1.0.0 (0.x) for extended periods. This signifies that the library’s public API has not stabilized and that breaking changes, which do not guarantee backward compatibility, can occur at any time. For projects with production dependencies, this acts as a factor that increases potential maintenance costs and risks.

  • Variability in Documentation: Despite the ability to generate standardized documentation via cargo doc, the quality of actual crate documentation varies widely. Some crates lack specific usage examples or explanations of their design philosophy beyond an API list, forcing developers to analyze the source code directly to use the library effectively. This can undermine the original purpose of using a library, which is to improve productivity.

  • Sustainability Issues in Maintenance: A problem common to many open-source ecosystems, even core crates are often maintained by a small number of volunteers. If a key maintainer discontinues the project for personal reasons, there is a risk that follow-up actions for security vulnerabilities or major bugs could be delayed for a long time. This can affect the stability of the entire ecosystem that depends on that crate.

2. Analysis of Criticism of Ecosystem Issues and Observed Response Patterns

When criticism of the qualitative problems of the ecosystem is raised, a certain discourse pattern is often observed in various public discussion spaces, such as some online forums, that tends to lead the discussion in a direction different from the technical substance of the problem. This tends to lead the discussion in a direction different from the technical substance of the problem.

  • Shifting Responsibility through ‘Encouraging Participation’: Responses like “Pull Requests are welcome” or “If you need it, contribute it yourself” are positive expressions that encourage the core open-source value of voluntary participation. However, when these expressions are used as an answer to legitimate criticism about a library’s flaws or lack of documentation, they can also function as a rhetorical device to shift the responsibility for solving the problem to the original critic. Considering the reality that not all users have the expertise or time to modify a library, such a response can stifle the circulation of constructive feedback.

  • The Representativeness Problem of Success Stories and a Statistical Perspective: In response to criticism about the overall qualitative maturity of the ecosystem, a counter-argument is sometimes made by presenting a few highly successful core crates like tokio or serde. It is certainly meaningful that these success stories show the potential of the Rust ecosystem and the high level of quality it can achieve. It is certainly meaningful that these success stories show the potential of the Rust ecosystem and the high level of quality it can achieve. However, this line of argument needs to be critically examined from the perspective of the ‘representativeness of the sample.’ It is difficult to say that a few exceptionally successful cases represent the average maturity of the entire ecosystem, which consists of numerous libraries, or the reality that a typical developer faces. This is not simply to point out a logical fallacy, but rather to pose an engineering and statistical question of whether a particular sample (success stories) is sufficient to describe the characteristics of the entire population (the ecosystem). This approach can lead to an overestimation of the current state of the ecosystem by limiting the focus of the discussion to a few top-tier cases, instead of taking a comprehensive view of the realistic problems faced by individual libraries.

5.6 Technical Challenges in the Development Toolchain and Productivity

The developer experience of the Rust language is accompanied by several technical challenges along with its powerful features. These challenges can have a real impact on the development productivity of large-scale projects in particular. This section analyzes the main issues in terms of the compiler’s resource usage, IDE integration and debugging environment, and the flexibility of the build system.

1. Compiler Resource Usage and Its Impact

The Rust compiler (rustc) tends to require a significant amount of time and memory resources during the compilation process. This stems from the fundamental design of the language, including the monomorphization strategy to implement the ‘Zero-Cost Abstractions (ZCA)’ principle and its dependency on the LLVM backend.

  • Compile Time: Monomorphization generates code for each generic type, increasing the amount of code the compiler must process and optimize. This lengthens the development feedback loop of ‘edit → compile → test,’ which can be a factor that hinders developer productivity, especially as the project size grows. While tools like cargo check provide fast syntax checking, a full build and test can still take a long time.

  • Memory Usage: High memory usage during compilation can cause problems in resource-constrained development environments (personal laptops, low-spec CI/CD build servers, etc.). In large-scale projects, the compiler process may exceed the system’s available memory, leading to it being forcibly terminated by the operating system’s OOM (Out of Memory) Killer. This is a factor that undermines the stability of the development experience.

However, it is worth noting that these costs are not fixed. The Rust project and community recognize compile time as a key improvement area and are continuously working to address it. Representative examples, such as the development of the Cranelift backend to improve debug build speeds and attempts to enhance the parallel processing capabilities of the rustc compiler itself, show that this engineering trade-off is not a static problem but is being dynamically managed.

2. IDE Integration and Debugging Environment: The Cost Behind the Abstractions

When discussing Rust’s developer experience, the IDE integration and debugging environment is an area that clearly demonstrates how the language’s core design philosophy imposes concrete costs on a developer’s actual workflow. While on the surface it appears to support modern language servers and standard debuggers, a closer look reveals that Rust’s complexity and abstraction model introduce points of cognitive load and productivity decline.

The Reality and Limitations of the Language Server (rust-analyzer)

The leading language server, rust-analyzer, provides powerful features such as code completion, type inference, and error checking by analyzing Rust’s complex type system and macro features in real time. It is regarded as a core tool that has significantly improved the productivity of the Rust ecosystem.

However, the very depth of this analysis acts as a cost. rust-analyzer must keep a vast amount of code, including project dependencies, resident in memory and must recalculate complex trait resolution and macro expansion every time the developer modifies the code. This leads to the following practical problems:

  • High Resource Usage: In large-scale projects, the rust-analyzer process itself can occupy several gigabytes (GB) of memory, which can be a burden in resource-constrained development environments.
  • Analysis Instability: In code where complex generic types or procedural macros are heavily used, it may fail to infer types or produce inaccurate diagnostics, leading to situations where the developer relies on the final diagnosis of the compiler (rustc) rather than trusting the language server’s results.

This can be interpreted not so much as a flaw in rust-analyzer itself, but as an inherent limitation of a language server that must handle the compiler’s heavy tasks in real time, and as evidence of the high complexity of the Rust language.

The Trade-off between Abstraction and Debugging

Rust’s ‘Zero-Cost Abstractions’ principle generates its cost for the developer during the debugging process. Although standard debuggers like LLDB or GDB are used, the experience of debugging Rust’s abstracted types differs from that of other languages.

For example, when inspecting a variable of type Vec<String> in a debugger, a developer in an integrated IDE environment for Java or C# might expect to see the logical contents, such as ["hello", "world"]. In a Rust debugger, however, what is exposed is the raw memory layout of the Vec struct: internal implementation details such as a pointer (ptr) to heap memory, the total allocated capacity, and the current number of elements, length.

This experience creates a cognitive load for the developer, who must interpret the low-level memory structure shown by the debugger to understand the program’s logical state. This is a clear trade-off where the price of eliminating runtime costs for abstractions manifests as a degradation in debugging convenience.

The Fundamental Challenge of Debugging Asynchronous Code

This problem is further intensified when debugging async/await code. As analyzed in Section 5.3, Rust’s async functions are transformed by the compiler into a large state machine. As a result, traditional stack-based debugging becomes difficult to apply effectively.

Even when execution is paused at an error point and the call stack is examined, the logical flow of a developer-written function_a calling function_b does not appear. Instead, what is visible are the internal functions of the async runtime’s scheduler (e.g., in tokio) and the calls to the poll function of a state machine generated by the compiler, which are difficult for a developer to interpret directly. Consequently, it becomes difficult to answer the fundamental question, “How did the execution get to this point?”

This stands in stark contrast to other mature ecosystems, such as C#’s Visual Studio or Java’s IntelliJ IDEA, which reconstruct and display the logical call stack for asynchronous code. The asynchronous debugging environment in Rust can be considered a prime example of how a design philosophy that minimizes runtime overhead can lead to significant complexity costs during the development and maintenance phases.

3. Flexibility of the Build System (Cargo)

Rust’s official build system, Cargo, provides high productivity based on the ‘convention over configuration’ philosophy, such as standardized project management and easy dependency resolution. This is a clear advantage of Cargo.

However, this advantage can act as a rigidity when the project’s requirements go beyond the standard scope. In cases where non-standard build procedures are needed, such as complex code generation or special integration with external libraries, it is often difficult to handle them flexibly with just build.rs scripts. Furthermore, in a large monorepo environment, the combination of feature flags can become complex, making dependency management another source of maintenance cost. This can be a constraint in large-scale industrial environments that need to respond to various build scenarios.

These factors demonstrate that the developer experience Rust offers comes with clear strengths alongside realistic technical challenges. Therefore, it is important to understand these as the results of differing design philosophies, rather than superficially evaluating the merits of a development environment. The following section will move away from the simplistic viewpoint of defining each ecosystem by a single philosophy. Instead, it aims to explore the essence of the debate more deeply by fairly comparing the two options—’decoupled toolchain’ and ‘integrated experience’—while also considering the realistic variable of ‘maturity’.

5.7 A Fair Comparison of Development Environments: The Intersection of Maturity and Design Philosophy

The previous section analyzed the technical challenges within Rust’s development environment. However, such analysis often risks falling into a simplistic dichotomy that compares only the most prominent features of each ecosystem, such as “the integrated IDEs of Java/C# are powerful, while Rust’s VS Code environment is flexible.” This approach overlooks the reality that both ecosystems offer two options: a ‘decoupled toolchain’ and an ‘integrated experience.’

Therefore, a fair comparison is only possible when we evaluate each philosophy on a like-for-like basis, while also considering the crucial variable of ‘ecosystem maturity.’

1. First Comparison: The ‘Decoupled Toolchain’ Environment (VS Code)

The advent of the Language Server Protocol (LSP) has provided a foundation for all languages to compete from an equal starting point in lightweight editors like Visual Studio Code. In this environment, the reality of each ecosystem is as follows:

  • For Java/C#: The Eclipse JDT LS, Red Hat’s Java extension, and C#’s Roslyn LSP have achieved a high degree of stability and maturity through years of development and support from major corporations. They provide reliable code completion, diagnostics, and basic refactoring capabilities, even in complex enterprise projects.

  • For Rust: rust-analyzer is a key driver behind the Rust ecosystem’s rapid growth. However, as analyzed in Section 5.6, it still faces challenges in terms of maturity, sometimes exhibiting instability or requiring significant system resources due to the language’s inherent complexity (e.g., macros, trait resolution).

  • Analysis: Under the same condition of a ‘decoupled toolchain,’ the LSPs for Java/C# demonstrate higher maturity, having evolved over a longer history on top of relatively stable language specifications. In contrast, rust-analyzer bears the burden of solving more complex linguistic challenges. This highlights the differences in each ecosystem’s historical path and technical challenges, rather than the superiority of one over the other.

2. Second Comparison: The ‘Integrated Experience’ Environment (Professional IDEs)

Both ecosystems also offer highly integrated environments that go beyond the capabilities of LSP.

  • For Java/C#: IntelliJ IDEA and Visual Studio, building on decades of accumulated experience, offer ‘project intelligence’ that goes beyond simple code analysis. The intelligent refactoring, optimized debugging, and profiling experiences, all provided with a semantic understanding of the code, are why these IDEs are classified as ‘development platforms.’ This represents the high level of maturity that the ‘integrated’ philosophy can achieve.

  • For Rust: JetBrains’ RustRover and CLion clearly show that the Rust ecosystem also has an ‘integrated experience’ option. These IDEs do not rely solely on rust-analyzer but attempt to provide enhanced debugger integration and intelligent refactoring features through their own analysis engines. This is a significant step forward in improving the Rust developer experience.

  • Analysis: In this area, the ‘maturity gap’ becomes more apparent. While RustRover has great potential, it is still in its early stages compared to IntelliJ’s Java support. It is a realistically difficult task to implement decades of accumulated refactoring patterns and debugging know-how from the Java ecosystem in a short period. This can be interpreted not as a technical limitation of Rust, but as a natural process that all growing technologies go through.

3. Conclusion: Reframing the Comparison

Directly comparing “Java/C#’s integrated IDEs” with “Rust’s VS Code” is an asymmetrical frame that cross-compares the most mature part of one ecosystem with the most popular part of another.

The points derived from a fair comparison are as follows:

  1. Both ecosystems offer development environments based on both philosophies.
  2. In both the ‘decoupled toolchain’ and ‘integrated experience’ domains, the Java/C# ecosystem exhibits a higher degree of maturity due to a longer history and greater investment.
  3. The Rust ecosystem’s development environment is evolving rapidly but faces maturity challenges stemming from the language’s inherent complexity and a shorter historical timeline.

Therefore, to attribute the differences between the two development environments to a matter of ‘dependency’ on one side, or to the superiority of a specific philosophy, is a premature conclusion. As analyzed in the text, the crux of the matter is that each ecosystem is at a different ‘stage of maturity.’ The Java/C# ecosystem, through significant time and investment, has achieved a high degree of polish in both ‘integrated’ and ‘decoupled’ approaches. In contrast, the Rust ecosystem is in a phase of rapid growth as it tackles the inherent complexity of its language. A mature engineering evaluation must begin by acknowledging this complex reality and then selecting the tools and philosophies best suited to a given project’s requirements.

6. Analyzing the Real Costs of ‘Zero-Cost Abstractions’

6.1 The Mechanism of Cost Shifting: The Role of Monomorphization

One of Rust’s core design principles is ‘Zero-Cost Abstractions (ZCA).’ This means that even when a developer uses high-level abstraction features like Generics or Iterators, it should not cause any degradation in the program’s runtime performance.

This principle is not unique to Rust but has deep roots in the design philosophy of C++. The principle proposed by Bjarne Stroustrup, the creator of C++, “You don’t pay for what you don’t use,” is the essence of ZCA. C++ has long implemented a way to eliminate runtime overhead by generating code at compile time through features like Templates.

Rust, just as it inherited the ownership model, has inherited this ZCA philosophy and developed it in a direction that also guarantees memory safety by combining it with the ownership and borrow checker. However, the term ‘zero-cost’ means ‘zero runtime cost,’ not that the cost required for abstraction does not exist at all. It is more accurate to understand Rust’s ZCA as a mechanism of cost-shifting, which secures runtime performance but transfers that cost to other stages of the development cycle.

At the core of this cost shifting is a compilation strategy called monomorphization. This is a method where, when compiling generic code like Vec<T>, separate, specialized code is generated for each concrete type used in the code, such as Vec<i32> and Vec<String>. While this strategy ensures high execution speed by eliminating indirect costs like runtime type checking or virtual function calls, it generates two main costs:

  1. Increased Compile Time: The compiler must duplicate the code for each generic type used and optimize each one individually. This increases the amount of code the compiler (especially the LLVM backend) has to process, which is a major cause of increased overall compile time.
  2. Increased Binary Size: All the specialized code generated is included as-is in the final executable file. This results in multiple copies of code with the same logic, which increases the size of the final binary. This is particularly noticeable when combined with static linking.

As an alternative to monomorphization, Rust provides a dynamic dispatch method using trait objects (&dyn Trait). This method generates a single function instead of duplicating code and calls the required implementation at runtime, thus offering a practical trade-off of slightly reduced runtime performance in exchange for shorter compile times and smaller binary sizes.

In conclusion, Rust’s ‘zero-cost abstraction’ is a product of a design philosophy that prioritizes runtime performance. However, the costs of increased compile time and binary size that occur in this process have a real impact on development productivity and the deployment environment, and this aspect of cost shifting is an important factor that must be considered when evaluating the ZCA principle. This is a clear design trade-off, paying the costs of compile time and binary size to achieve the goal of ‘zero runtime cost.’

6.2 Binary Size Analysis: The Impact of Design Principles on Application Domains

Rust programs tend to have larger executable binaries compared to programs with similar functionality written in C/C++. This is a critical consideration in resource-constrained systems programming domains where Rust is often discussed as a major alternative to C/C++. This section will analyze the technical reasons for this phenomenon and examine its ripple effects through a comparison of specific case studies.

1. Technical Cause: ABI Instability and Static Linking

One of the fundamental reasons for the increase in Rust binary size is the design characteristic that the ABI (Application Binary Interface) of the standard library (libstd) is not kept stable. The C language, based on a stable libc ABI for decades, supports dynamic linking, where multiple programs share a common shared library installed on the system. Thanks to this, the executable file of a C program can maintain a small size by including only its own unique code.

In contrast, Rust has not stabilized its ABI to allow for the free modification of the internal implementation of libstd for the sake of rapid improvement and evolution of the language and library. This is a design choice that prioritizes ‘rapid evolution’ over ‘stable compatibility.’ As a result of this choice, static linking, where every program includes the necessary library code within its executable file, has been adopted as the default method instead of dynamic linking, which is difficult to guarantee version compatibility for. Therefore, even a simple program will have the relevant functions of libstd all included in the binary, increasing its size.

2. Case Study: A Comparison of CLI Tools and Core Utilities

The impact of this design can be confirmed through a comparison of the sizes of actual programs.

Case 1: grep vs. ripgrep ripgrep is a high-performance text search tool written in Rust, known for its superior performance compared to the C-based grep. However, while the size of a dynamically linked grep on a typical Linux system is several tens of kilobytes (KB), a statically linked ripgrep can be several megabytes (MB). While this provides the convenience of dependency management for single-application deployment, it can become a burden of increased total capacity in a scenario of replacing all the basic tools of an operating system.

Case 2: BusyBox vs. uutils In extremely resource-constrained embedded Linux environments, BusyBox, which provides many commands like ls and cat in a single binary, is widely used. BusyBox, written in C, is very small, with a total size of less than 1MB. In contrast, uutils, developed in Rust for a similar purpose, has a size of several MB. Of course, the specific size can vary depending on the version of each project and the compilation environment, but this tendency is a structural result stemming from the differences in the standard library design and default build methods of the two languages. The table below provides a comparison based on Alpine Linux packages.

Table 6.2: Package Size Comparison of Major Core Utility Implementations (Based on Alpine Linux v3.22)8

Package Language Structure Installed Size (approx.)
busybox 1.37.0-r18 C Single binary 798.2 KiB
coreutils 9.7-r1 C Individual binaries 1.0 MiB
uutils 0.1.0-r0 Rust Single binary 6.3 MiB

This data shows that Rust’s default build method is significantly different from the requirements of the ultra-lightweight embedded environment that BusyBox targets.

3. Size Reduction Techniques and Their Trade-offs

There are several techniques to reduce the size of a Rust binary, which are shared through guidelines such as min-sized-rust. The main techniques are as follows:

  • Changing the panic handling strategy (panic = 'abort'): Instead of unwinding the stack when a panic occurs, the program is immediately terminated, removing the related code and metadata. This reduces the size but skips the process of safe resource cleanup.
  • Excluding the standard library (no_std): Does not use libstd, which provides OS-dependent features like heap memory allocation, threading, and file I/O. This can dramatically reduce the size, but it comes with the constraint that core data structures and features like Vec<T> and String must be implemented directly or depend on external crates.

Thus, to implement a small binary on par with C/C++ in Rust, one must intentionally disable the rich features and some of the safety measures that the language provides by default. This suggests that Rust’s default design philosophy places more emphasis on the richness of features and runtime performance than on small binary size.

The increase in compile time and binary size caused by the ‘zero-cost abstraction’ principle and its implementation method, monomorphization, is a representative example of Rust’s design philosophy.

These costs are not a ‘maturity problem’ stemming from the immaturity of the technology, but a clear ‘inherent trade-off’ that intentionally sacrifices other resources like ‘development time’ and ‘deployment size’ to secure the top-priority value of ‘runtime performance.’ This clearly demonstrates the fundamental principle of engineering that “costs do not disappear, they are merely shifted elsewhere.” Therefore, a developer must understand this mechanism of cost shifting behind the term ‘zero-cost’ and carefully evaluate whether Rust’s design philosophy aligns with the constraints required by their project (e.g., fast compile speed, small binary size).

7. Realistic Constraints of Industrial Application

7.1 Embedded and Kernel Environments: Application Realities and Engineering Challenges

One of the main areas where Rust is evaluated as an alternative to C/C++ is in embedded systems and operating system kernel development. However, applying Rust in these two fields presents several significant engineering challenges. Just as C cannot use user-space standard libraries like glibc in a kernel environment, it is natural that Rust also cannot use its standard library (libstd), which depends on operating system features.

Therefore, the real issue is not the existence of no_std itself, but the disconnect in the development model and the associated costs that most Rust developers, accustomed to the std environment, face when switching to no_std. While the C development model consistently assumes a low-level environment from the outset, for Rust developers familiar with the rich std ecosystem, the transition to no_std demands a cognitive cost similar to learning a different language. This is because the absence of core features like heap allocation, threading, and standard data structures (e.g., Vec<T>, String), as well as the inability to use numerous libraries that depend on std, severely restricts the available ecosystem.

A representative attempt to solve these challenges is the ‘Rust for Linux’ project, and its approach can be summarized by a few key characteristics:

  1. Building Safe Abstractions: The core of the project is to safely wrap the existing low-level, unsafe APIs written in C within the Linux kernel, leveraging Rust’s ownership and lifetime rules. For example, dangerous elements like the kernel’s memory allocation functions (kmalloc, kfree), locking mechanisms, and reference counting are abstracted into safe data structures similar to Rust’s Box<T>, Mutex<T>, and Arc<T>. This allows developers to focus on high-level logic, making full use of Rust’s compile-time safety checks, instead of directly dealing with the kernel’s complex and error-prone internal operations.

  2. Systematic Use of unsafe: However, at the lowest level of this abstraction layer, the use of unsafe code is unavoidable for calling C functions or directly accessing hardware registers. This is not a failure of Rust but the result of a systematic FFI (Foreign Function Interface) design intended to interoperate with the C ecosystem, which has been built up over decades. In other words, the key strategy is to isolate risk factors at a specific boundary, enabling the development of safe code on top of it.

  3. Real-World Application and Cultural Challenges: Building on this foundation, Rust is now being adopted, either experimentally or actively, in core parts of real systems, such as Android’s Binder IPC driver and the GPU driver for Apple M1/M2 chips. This demonstrates that Rust is proving its value not just in the periphery of the kernel, but also in complex, performance-sensitive areas. Of course, this process involves more than just technical hurdles. The skepticism of some long-time C kernel developers and the cultural and philosophical debates on the Linux Kernel Mailing List (LKML) are also a significant part of the integration reality.

To quantitatively analyze the actual state of integration in the Linux kernel, the source code of Linux kernel v6.15.5 (as of July 9, 2025), distributed by kernel.org, was analyzed using the cloc v2.04 tool.9 The analysis showed that the total number of pure code lines (SLOC), excluding comments and whitespace, was 28,790,641, of which Rust code accounted for 14,194 lines, or about 0.05% of the total.

This figure shows the status at a specific point in time. The integration of Rust into the kernel is an ongoing project, so this proportion may change in the future. Nevertheless, this data is significant in that it objectively shows the relative scale of Rust within the kernel’s vast C codebase and the early stage of integration as of mid-2025. Of course, the quantitative proportion of code does not directly represent the qualitative importance or technical impact of that code. A qualitative look at the content of the currently included code shows that its role is mainly focused on building the basic infrastructure for writing drivers. On the other hand, how criticism based on such objective data is received and defended within certain technical discourses will be analyzed again in the case studies of Section 8.4.

The table below summarizes the distribution of the main languages with a high proportion of code lines in that kernel version.

Table 7.1: Proportion of Major Languages in Linux Kernel v6.15.5 (Unit: Lines, %)¹

Rank Language Lines of Code Percentage (%)
1 C & C/C++ Header 26,602,887 92.40
2 JSON 518,853 1.80
3 reStructuredText 506,910 1.76
4 YAML 421,053 1.46
5 Assembly 231,400 0.80
… … … …
14 Rust 14,194 0.05

¹Based on a total of 28,790,641 lines of code. Some languages are omitted.

7.2 Mission-Critical Systems and the Absence of International Standards

In mission-critical systems fields such as aviation, defense, and medicine, where high reliability is required, the maturity of the industry standard and ecosystem is an important criterion for choosing a language, in addition to technical performance.

These fields often require compliance with international standards (e.g., ISO/IEC) to ensure the stability and predictability of software. A standardized language has a fixed specification, making long-term maintenance easier and forming the basis of a commercial ecosystem where various vendors provide compatible compilers, static analysis tools, and certification support services. Languages like C, C++, and Ada have such standardization procedures and mature vendor ecosystems.

However, Rust is not a language established as an international standard, and it has adopted a model of flexibly changing the language specification for the sake of rapid development. While this ‘rapid evolution’ model is advantageous for short-term feature improvements, it can conflict with the requirements of the mission-critical field, which is extremely conservative about change and prioritizes long-term stability. As a result, compliance with related regulations and certification procedures becomes more complex, and it becomes difficult to receive support from professional commercial vendors, which acts as a structural barrier to full-scale entry into this field.

7.3 Realistic Barriers to Adoption in General Industry

There are the following realistic barriers to Rust’s spread beyond specific fields to the general industry as a whole.

  1. Talent Pool and Training Costs: The pool of skilled Rust developers is still limited compared to mainstream languages like Java, C#, and Python. For companies, this leads to difficulties in hiring and high labor costs. Furthermore, to transition existing developers to Rust, they must accept a high learning cost for unique concepts like the ownership model and an initial period of reduced productivity.

  2. Maturity of the Enterprise Ecosystem: In some areas, the ecosystem of essential tools for large-scale enterprise application development, such as ORM (Object-Relational Mapping) frameworks, cloud service SDKs, and authentication/authorization libraries, is not yet as mature as that of Java or .NET. This is a factor that makes companies that prioritize development speed and stability hesitant to adopt it.

  3. Legacy System Integration and Migration Costs: Most companies already operate vast legacy systems built with C++, Java, etc. A full rewrite of these systems in Rust would involve astronomical costs and unpredictable risks. Therefore, gradual integration or interoperability is a realistic alternative, but inter-language interoperability through FFI (Foreign Function Interface) itself contains considerable technical complexity and the potential for errors.

These factors are important business and engineering constraints that a real company must consider when choosing a technology stack, separate from the technical excellence of the language.

7.4 A Multifaceted Analysis of the “Big Tech Adoption” Narrative: Context, Limits, and Strategic Implications

The powerful and frequent argument for Rust’s practicality and future value is the adoption by world-renowned technology companies like Google, Microsoft, and Amazon. The fact that these companies use Rust is undoubtedly an important indicator that proves Rust’s technical value and its ability to solve specific problems.

However, for an engineering evaluation, one must analyze not just the fact of ‘which company uses it,’ but the specific ‘context,’ ‘scale,’ and ‘conditions’ of that adoption. Such a multifaceted analysis allows for a deeper understanding of the technical reality and strategic implications hidden behind the narrative of ‘big tech adoption.’

1. A Critical Examination of the Context, Scale, and Conditions of Adoption

First is the context of application. These companies are not introducing Rust across all their systems and products, but are applying it ‘selectively’ to specific areas where Rust’s strengths are most maximized. For example, these include low-level components of an operating system kernel, security-sensitive parts of a web browser’s rendering engine, and high-performance infrastructure where even the slight delay of a garbage collector is not permissible. This means that Rust is being utilized as a ‘strategic tool,’ not a ‘total replacement,’ in the reality that these companies still use C#, Java, Go, and C++ as their main languages in much broader areas.

Second is the scale of adoption. The word ‘adoption’ often implies widespread acceptance throughout an organization, but the reality can be different. Compared to the total number of software projects or the size of the developer talent pool in these companies, the proportion occupied by Rust is still in a growth phase. The successful adoption by a few core teams should not be magnified by a ‘halo effect’ through the company’s logo, as if it were the standard technology of the entire organization.

Third are the conditions of adoption. Big tech companies possess immense resources to afford the costs of introducing a new technology. This includes the cost of developer training for a high learning curve, the cost of internal tooling and library development to fill gaps in the ecosystem, and the temporal and financial leeway to endure an initial drop in productivity. Presenting the success stories of big tech companies as ‘universal evidence’ that can be equally applied to the majority of general companies with limited personnel and budgets, without considering this reality of resources, may be to overlook the ‘representativeness of the sample’ problem. It is difficult to assume that the success observed in the special sample group of big tech companies will be equally reproduced in the population of the entire industrial ecosystem. This also connects to the ‘representativeness of the sample’ problem pointed out in Section 5.5.

2. The Implications of Strategic Adoption: Proving Value Beyond a ‘Niche Market’

However, the above critical analysis should not lead to the conclusion that ‘the adoption by big tech is insignificant.’ On the contrary. The very fact that these companies have ‘strategically chosen’ Rust is powerful evidence of Rust’s value.

The key is which problem these companies introduced Rust to solve. Google’s Android, Microsoft’s Windows kernel, and the Chrome browser operate on top of existing C++ codebases of hundreds of millions of lines. Ensuring memory safety in these systems without performance degradation has been a very difficult challenge that has not been solved for decades.

In this situation, Rust was chosen as ‘the most realistic, or perhaps the only, technical solution that can gradually introduce memory safety in a scalable way to a large codebase while maintaining the existing performance and control level of C++.’ This proves that Rust is not just another ‘new language,’ but has the unique ability to solve the most serious and costly problems faced by the industry’s top engineering organizations.

This choice can be interpreted as a significant leading indicator that the fundamental paradigm of systems programming is changing, going beyond solving the problems of a ‘niche market.’

3. Conclusion: The Need for a Balanced Evaluation

In conclusion, the case of big tech adoption of Rust requires a two-sided analysis. On the one hand, one must be wary of using it as evidence of ‘universal superiority’ in all problem situations and clearly recognize its specific context and limitations. On the other hand, one must acknowledge that this selective adoption proves Rust’s unique value in solving the most important and difficult problems in the systems programming field and is a powerful signal leading a paradigm shift.

A mature engineering judgment should be made through such a multifaceted analysis instead of simply relying on the authority of a particular brand, and it should start from an objective evaluation of both the limitations and the potential of a particular technology.

The constraints of industrial application for Rust, as analyzed in this chapter, are the result of a complex interplay of two factors: ‘maturity problems’ and ‘inherent trade-offs.’

The barrier to entry into mission-critical systems, which arises from the absence of international standards or ABI stability issues, is closer to an ‘inherent trade-off’ stemming from Rust’s core development model that prioritizes ‘rapid evolution.’ This is a structural characteristic that is difficult to resolve in the short term.

On the other hand, the lack of a skilled developer talent pool or an incomplete library ecosystem in certain enterprise domains is a typical ‘maturity problem’ that can be gradually alleviated as the adoption of the technology spreads and the community grows.

In conclusion, for Rust to spread to broader industrial fields beyond its current success, it faces the challenge of overcoming both of these types of barriers. Along with continuous efforts for the maturity of the ecosystem, long-term consideration is needed on how the language’s core design philosophy can be harmonized with the requirements of various industries.

Part 4: A Case Study on the Formation of Tech Community Discourse, The Rust Ecosystem

Having analyzed the technical features of Rust and the engineering trade-offs behind them up to Part 3, Part 4 will now shift its focus to critically deconstruct the social phenomenon surrounding Rust, namely, the ‘discourse.’

The analysis in this part will be approached as a case study examining the formation process of defensive discourse in a particular technical community and its logical patterns. It is clarified that the object of analysis is not the official position of the Rust project, but is limited to a specific tendency observed in some online discussion spaces. It is clearly stated that this is not an attempt to over-interpret the voices of a few as the opinion of the entire community. Nevertheless, the reason this book focuses on such informal discourse is that, even if it is the voice of a few, it shapes a new developer’s first impression of the technology and has a real impact on their experience of entering the ecosystem. Furthermore, this public discourse has significant analytical value because it can become the training data for Large Language Models (LLMs), leading to the technical re-learning and amplification of existing biases. This part aims to achieve a deep understanding of the universal formation process of such technical discourse through the specific case of Rust. Chapter 8 will analyze how the ‘silver bullet narrative’10 is formed and how it functions as a collective defense mechanism when faced with criticism, and Chapter 9 will consider the realistic impact of this discourse on a developer’s technology choices and the sustainability of the ecosystem. Finally, Chapter 10 will synthesize all the preceding analyses to present the challenges and prospects for the Rust ecosystem and conclude.

Ultimately, Part 4 aims to help developers cultivate a more mature and balanced perspective by moving beyond blind advocacy or criticism of a particular technology and understanding the way a technology ecosystem operates.

8. The “Silver Bullet Narrative” and the Formation of Collective Defense Mechanisms

8.1 The Formation Process and Effects of the ‘Silver Bullet Narrative’

The analysis of the ‘silver bullet narrative’ in this chapter will begin by clearly defining its scope. It must be stated that this analysis does not address the official positions of the Rust Foundation or its core development team, nor is it an attempt to generalize the entire Rust community as a single entity. The focus of this chapter is a specific discourse that shows a different tendency from the official self-critical culture of the Rust project.

In fact, Rust’s core developers and the Foundation recognize the complexity of async, compilation times, and toolchain issues described in the preceding chapters of this book as key areas for improvement. They specify these technical limitations and seek solutions with the community through the Request for Comments (RFC) process and official blogs.

Therefore, the subject of this chapter’s analysis is confined to the defensive or generalized rhetoric of certain supporters observed on some online tech forums and social media, separate from these official improvement efforts.11 As it is difficult to measure the quantitative prevalence of this informal discourse, this analysis focuses on deconstructing its ‘logical structure’ and ‘effects’ rather than its ‘frequency.’

As analyzed in Section 2.3, one of the factors that influenced Rust’s growth was a persuasive narrative centered on values such as ‘safety without performance degradation.’ This narrative is considered to have contributed to the growth of the ecosystem by shaping the community’s identity and encouraging contributions from volunteers.

However, when this narrative is confronted with external criticism or technical limitations, a tendency is sometimes observed for it to simplify into a ‘silver bullet narrative’10—the idea that “Rust solves all systems programming problems”—and lead to collective defense mechanisms. To analyze the social dynamics underlying this phenomenon, certain concepts from social psychology can be utilized as an analytical framework. This is not an attempt to ‘diagnose’ the psychology of any particular group or individual, but rather an approach to explain the structure and effects of discourse formation that appears in technology communities with strong identities.

For example, the theory of cognitive dissonance is a concept that describes the state an individual experiences when faced with information that conflicts with their efforts or beliefs. Applying this framework, we can imagine a situation where a developer has invested considerable time and effort to overcome Rust’s steep learning curve. Facing criticism about the language’s shortcomings or limitations after such a significant investment can trigger a state of dissonance that conflicts with the motivation to justify one’s efforts. As a result, the individual may show a tendency to resolve this state by emphasizing the advantages of their chosen technology and downplaying its disadvantages.

Furthermore, from the perspective of social identity theory, when mastery of a particular technology becomes linked to a developer’s professional identity, the community tends to form an ‘in-group’ with strong bonds. In this case, external criticism may not be received as a technical review but can be perceived as a challenge to the in-group’s values or identity. This dynamic can be a factor in the formation of defensive discourse that relatively devalues other tech ecosystems, the ‘out-groups.’

This in-group/out-group structure can be further reinforced through the ‘echo chamber effect’ in certain online spaces. An echo chamber refers to a phenomenon where similar opinions are amplified within a closed system through repetition. In this environment, information that aligns with the community’s dominant narrative (e.g., ‘Rust is the safest systems language’) is actively shared, while critical opinions or alternative perspectives tend to be marginalized in the discussion. Consequently, participants’ existing beliefs are further strengthened, which can function as a mechanism to solidify the ‘silver bullet narrative’ and maintain a defensive posture against external criticism.

It appears that the ‘silver bullet narrative’ is reinforced through specific information framing built upon this psychological foundation.

A Structural Analysis of the Causes of Selective Framing

The phenomenon of Rust-related discourse selectively emphasizing a confrontational framing with C/C++ and not giving significant weight to alternatives like Ada/SPARK is difficult to explain solely by the intention to ‘secure discursive leadership.’ The following structural causes, inherent in the way the developer ecosystem operates, work in combination here.

  1. Asymmetry in Information Accessibility and Learning Resources: The process by which a software developer learns and compares specific technologies is heavily dependent on the quantity and quality of available information. C/C++ has a vast amount of books, university lectures, online tutorials, and community discussion materials accumulated over decades. Rust, too, has rapidly built a rich learning ecosystem through its official documentation (“The Book”) and a vibrant community. In contrast, since Ada/SPARK has primarily developed around specific high-integrity industry sectors like aviation and defense, modern learning materials and public community discussions that are easily accessible to general developers are relatively scarce. This marked difference in information accessibility acts as a fundamental background that naturally leads developers to perceive C/C++ as the main point of comparison.

  2. Industrial Relevance and Changing Market Demands: Technical discourse tends to form around the technologies that are most actively used and competing in the current market. C/C++ is the foundational technology for a wide range of industries, including operating systems, game engines, and financial systems, while Rust is emerging as an alternative to C/C++ in new high-performance systems areas like cloud-native, web infrastructure, and blockchain. In other words, the two languages are in a clear relationship of direct competition or are considered as replacements in the actual industrial field. In contrast, the mission-critical systems market where Ada/SPARK is mainly used has different requirements and ecosystems from the general software development market, making the need for direct comparison relatively low.

  3. Educational Curriculum and Developers’ Shared Experience: In most computer science education curricula, C/C++ is adopted as the practical language for core subjects like operating systems, compilers, and computer architecture, giving it a role akin to a ‘lingua franca’ for programmers. Therefore, the memory management problems of C/C++ are a shared experience and a common point of concern for many developers. The reason the Rust discourse gains great sympathy when it points out the problems of C/C++ is that this shared background exists. In comparison, Ada is not covered in most standard educational curricula, so it is difficult to form a universal consensus among developers by using it as a point of comparison.

Synthesizing these structural factors, the C/C++-centric confrontational framing can be analyzed not as an intentional exclusion by a particular group, but as a natural result of the combined effects of the asymmetry of the information ecosystem, the realistic demands of the market, and the shared educational background of developers.

The Preemption of the ‘Memory Safety’ Agenda and Discursive Leadership

Another important result of this narrative formation process was the successful preemption of the ‘memory safety’ agenda in the field of systems programming.

Originally, many mainstream languages like Java, C#, and Go have provided memory safety by default through GCs and other means. However, in these ecosystems, ‘memory safety’ was a given premise and thus not a central topic of discussion.

Some discourses supporting Rust, in the context of a confrontational framing with C/C++, have continuously emphasized ‘memory safety’ as a core differentiator and the most important value of the language. As a result, an ‘agenda-setting’ effect occurred, where many developers came to clearly recognize the term ‘memory safety’ and its importance for the first time through Rust. This can be analyzed as a successful case of raising a specific value to the center of the discourse, leading public perception of that concept, and turning it into a powerful brand asset.

In conclusion, the ‘silver bullet narrative’ was effectively formed by some supporters through the methods of selective framing of comparison targets and preemption of a core agenda. While this contributed to publicizing Rust’s value and strengthening the community’s identity, it also leaves room for critical review that it may hinder a balanced view of the technology ecosystem.

Ripple Effects on the Information Ecosystem and AI Training Data

Once a dominant discourse about a particular technology is formed, it can spread beyond the boundaries of its community to affect the broader technology information ecosystem as a whole.

First, it affects the information accessibility of new learners. When searching for information on a specific field (e.g., safe systems programming), the discourse that is quantitatively dominant online is more likely to occupy the top search results. In this case, a learner will primarily encounter Rust as an alternative to C/C++, and may not become aware of the existence of other important technical alternatives that are discussed less, such as Ada/SPARK. This can act as a factor that limits the opportunity for a balanced technology choice.

Second, it can cause a bias in the training data of Large Language Models (LLMs). Since LLMs learn information based on vast amounts of text data from the internet, the quantitative distribution of the training data directly affects the model’s response generation tendency. If a framing that emphasizes the advantages of a particular technology (Rust) dominates the discourse, the LLM is more likely to mention Rust first or treat it as more important than other technical alternatives (Ada/SPARK) in response to questions like “What is the safest systems programming language?”, based on its frequency of appearance in the training data. This can lead to a result where an existing discursive bias is re-learned and amplified by artificial intelligence.

8.2 The Realistic Limits of the “Total Replacement” Narrative

The ‘silver bullet narrative’ often extends to the prospect that “Rust will ultimately completely replace existing systems programming languages.” However, this narrative of ‘total replacement’ may not sufficiently consider the following realistic constraints of the software ecosystem.

  • Technical Constraint: Dependency on the C ABI (Application Binary Interface) All major modern operating systems, hardware drivers, and core libraries use the C language’s calling convention as a standard interface. Rust, too, must necessarily use the C ABI to interoperate with this existing ecosystem. This means that Rust is in a structural relationship where it must realistically ‘coexist’ with or ‘integrate’ with the C ecosystem, rather than ‘replacing’ it.
  • Market Constraint: The Importance of the Existing Application Ecosystem The value of the software market is determined not by the language itself, but by the specific applications (games, professional software, etc.) created with it. The vast assets of commercial and open-source applications accumulated over decades in C/C++ act as a powerful market entry barrier that is difficult to overcome with technical superiority alone.

8.3 A Historical Precedent in Tech Discourse: The OS Wars of the 1990s-2000s

The formation of a powerful narrative and collective identity around a specific technology is not a phenomenon unique to Rust. It is a pattern that has been repeatedly observed throughout the history of technology. A prime example is the ‘Linux vs. Microsoft Windows’ competitive landscape of the 1990s and early 2000s.

At that time, various voices coexisted within the Linux community, but a single powerful narrative was formed through a current centered on values like ‘freedom and sharing.’ They saw themselves as a technical/moral alternative to the ‘giant monopoly,’ and this identity sometimes led to referring to a certain company as ‘M$.’12 The following similar patterns appeared in this narrative formation process.

  • Clear Confrontational Framing: A binary frame of ‘openness’ vs. ‘closedness,’ ‘hacker culture’ vs. ‘commercialism’ was used.
  • Technical Superiority: The ability to use a text-based CLI (Command-Line Interface) and compile a kernel was considered a measure of a ‘true developer’s’ competence, serving as a standard to distinguish them from user bases that relied on a GUI.
  • Defensive Attitude towards Criticism: Criticism of usability issues or hardware compatibility problems was often dismissed as the user’s ‘lack of effort’ or ‘lack of understanding.’ (e.g., “RTFM, Read The Fucking Manual”)13
  • Optimism about the Future: Regardless of objective market share, a belief in the inevitable victory of the ‘Year of the Linux Desktop’ was shared within the community.

This historical example helps to understand the universal phenomenon that occurs when the discourse of a particular technical community is formed around values and identity, going beyond technical advantages. This suggests that when analyzing some phenomena in the Rust community, it may be more objective to approach them from a socio-technical perspective rather than from an individual’s psychological characteristics.

8.4 An Analysis of Argumentation Patterns in Response to Critical Discourse

In a community where a favorable narrative about a particular technology is dominant, certain defensive response patterns may appear in response to critical discourse that opposes it. This can hinder constructive technical discussion and, further, can escalate into conflicts between communities. This section analyzes these response patterns and their consequences through typical examples that show how certain logical fallacies can manifest. These patterns are particularly often observed in numerous tech blog comments where multiple technologies are compared, or on major online platforms like X (formerly Twitter), Hacker News, and Reddit. The purpose of this section is not to verify the factual basis of any particular incident, but to exemplify the argumentation structures that appear in these public discussions by linking them to the logical fallacies in the Appendix.

Case Study 1: Rhetorical Defense Against Objective Data

Situation: In an online forum, objective data was presented showing that the proportion of Rust code in the Linux kernel was less than 0.1% according to a cloc tool analysis. Based on this, criticism was raised pointing out the realistic limits of the claim that “Rust will replace all systems programming.”

Observed Response Pattern: In response to this data-based criticism, some users tended to respond with the following rhetorical strategies:

  1. Red Herring: Instead of directly refuting the core of the criticism—’the low proportion of Rust’—they would shift the subject of the discussion by saying, “Other languages like Ada haven’t even made it into the kernel,” or they would question the motive of the criticism by saying, “The critic is a supporter of a certain language, so they are biased.”14
  2. Ad Hominem: Responses appeared that attacked the intelligence or character of the person who raised the criticism, rather than the content of the criticism, such as, “You lack the intellectual capacity to understand such logic,” or “Seeing that attitude, I can tell what your level is.”15
  3. Confirmation Bias-based Rebuttal: Rather than responding to the specific data on the proportion in the Linux kernel, they would try to defend the original claim by selectively presenting other positive examples, such as “Big tech companies like Google/MS use Rust.” This is closely related to ‘cherry picking,’ which selects only a few favorable cases, or the ‘hasty generalization fallacy.’

Analysis: The response patterns above correspond to representative logical fallacies that hinder rational discussion of technical facts. This is a case that shows that even criticism based on objective data can provoke an emotional and defensive reaction if it conflicts with the existing dominant narrative, rather than being accepted.

Case Study 2: The Boundary of the Definition of ‘Safety’ and Evasion of Discussion

Situation: A developer pointed out that a memory leak caused by a circular reference in Rc<RefCell<T>> could cause serious problems in a long-running server application, and criticized this as a practical limitation of Rust’s safety model. (Connects to the discussion in Section 3.3)

Observed Response Pattern: In response to this criticism of a practical limitation, some users showed a tendency to shift the focus of the discussion by concentrating on the ‘definition’ of the term rather than the technical substance of the problem.

  1. Argument by Definition: “Rust’s ‘memory safety’ means the absence of Undefined Behavior (UB). A memory leak is not UB, so this is an issue unrelated to Rust’s safety guarantee. Therefore, your point is off-topic.” In this way, they use the official technical definition of the language as a shield to evade discussion of a practical problem.
  2. Shifting Responsibility: “Creating a circular reference is the developer’s mistake, and Rust provides solutions like Weak<T>. It is unfair to blame the language’s limitations for the failure to correctly use the features provided by the tool.” In this way, the cause of the problem is entirely attributed to the individual developer’s responsibility.

Analysis: This response pattern uses a logical strategy called ‘definitional retreat’ to defend the core narrative of ‘safety.’ By bringing a ‘problem’ from a practical perspective into the frame of a technical ‘definition,’ it has the effect of defining the criticism itself as a ‘misunderstanding’ or ‘ignorance.’ This can block the path to a constructive engineering discussion, such as ‘What additional tools or analysis techniques can the ecosystem develop to prevent memory leaks?’, and make the problem seem like something that is ‘already solved or a trivial matter outside the scope of the guarantee.’

Case Study 3: The ‘Intellectual Honesty’ Problem and Inter-Community Conflict

Situation: A non-profit security foundation released a version of a high-performance video decoder, originally written in C, that was ported to Rust, and a controversy arose when they offered a prize for performance improvements.

The technical issues raised in this controversy and the resulting conflict can be summarized as follows:

  1. The Other Side of the ‘Safety’ and Performance Claims: The version ported to Rust touted ‘memory safety’ as its main value, but the core of its actual performance was the hand-written assembly code brought over as-is from the original C project. This core code was even being called through an unsafe block that bypassed Rust’s safety checks.
  2. Criticism of ‘Intellectual Honesty’: Strong criticism was raised about this structure, mainly from the community of the original C decoder developers. The core of the criticism was that “promoting it as if it were the achievement of ‘safe Rust,’ when the real source of performance is C/assembly code, is an act of intellectual dishonesty that does not properly acknowledge the contribution of the original project.”
  3. Limitations of the Maintenance Model: The Rust-ported version had a structure that required continuously backporting updates from the original C project manually. This faced fundamental criticism from the C developer community: “Is this not an asymmetrical contribution structure that relies on the original C project for core R&D while only utilizing its results?”

Analysis: This case shows that when the narrative formation of one technical community does not respect the engineering achievements of another community, a serious inter-community conflict can arise. The fact that the contribution of the original was not clearly stated for the sake of the ‘safe and fast’ narrative escalated into an issue of ‘intellectual honesty,’ which provoked a strong backlash from the original developers. This is an important case that shows how a technical debate can turn into an issue of community pride and trust.

8.5 Defining ‘Qualification’ and ‘Normality’: Gatekeeping and Discursive Exclusion

A certain discourse in response to technical criticism sometimes manifests by questioning the ‘qualification’ of the critic or the subject, rather than directly addressing the points raised. This is a rhetorical strategy that shifts the topic of discussion from technical validity to issues of identity and status, and it is primarily observed in two forms: ‘gatekeeping’ and ‘defining normality.’

1. Gatekeeping: Setting the Qualifications for a ‘True Developer’

Gatekeeping is a social act that excludes outsiders’ opinions from discussion by setting membership qualifications for a particular group. In technology communities, this appears as an attempt to question the validity of a criticism based on grounds such as the critic’s ‘lack of expertise.’

  • Case Analysis: When a game developer reflects on difficulties stemming from the immaturity of the ecosystem based on three years of Rust experience, a response like the following may appear:

    “You’re talking about systems programming, yet you’re only focusing on business logic. True systems programming is about directly handling core elements like event loops or schedulers. What you’re doing isn’t real systems programming.”

  • Discursive Function: This response does not address the raised issue (ecosystem immaturity) but instead presents a specific standard for ‘true systems programming.’ By asserting that the other person does not meet this standard, it questions the validity of the experience that forms the basis of the criticism. This can be seen as an example of the ‘No True Scotsman Fallacy,’16 where the subject of discussion is redefined to maintain an existing argument when a counterexample is presented. This gatekeeping has the effect of shifting the focus from the content of the criticism to the qualifications of the critic.

2. The Regulation of Normality: Evaluation Based on a Specific Ecosystem’s Standards

The ‘regulation of normality’ is an argumentative method that establishes the characteristics of a specific tech ecosystem as ‘normal’ or ‘standard,’ and then evaluates the approaches of other ecosystems against that criterion.

  • Case Analysis: When evaluating the toolchains of other languages, expressions such as the following may be used:

    “Frankly, this is a feature that any normal language should have by default.”

  • Discursive Function: The function of this argument is to establish the characteristics of the speaker’s familiar Rust ecosystem (e.g., the ‘decoupled toolchain’ centered around Cargo and rust-analyzer) as the standard for ‘normality.’ By applying this standard, the ‘integrated experience’ provided by the IDEs of the Java/C# ecosystems (IntelliJ, Visual Studio) can be assessed as deviating from the norm.

    This framing tends to omit the fact that the Java/C# ecosystems also support a ‘decoupled toolchain’ environment through VS Code and language servers. Consequently, it simplifies a reality where multiple approaches coexist into a dichotomous structure of ‘normal’ versus ‘abnormal,’ thereby laying the groundwork to emphasize the validity of a specific model.

In conclusion, discourses that seek to regulate ‘qualification’ and ‘normality’ may function to support a particular viewpoint rather than to provide a comprehensive analysis of technical facts. This type of argumentation can act as a barrier to the community’s acceptance of external technical perspectives, thereby leading to a more limited scope of discussion.

8.6 The 2023 Trademark Policy Controversy and a Reflection on Governance

In the process of an open-source project’s growth and institutionalization, conflicts can arise between existing informal practices and new official policies, putting the governance model to the test. The controversy surrounding the draft of the Rust trademark policy in 2023 is an important case study that illustrates this process.

In April 2023, the Rust Foundation released a draft of a new trademark policy regarding the use of the Rust name and logo and requested feedback from the community. However, as the perception spread that the content of the released draft was very restrictive compared to the community’s existing informal practices, it provoked considerable criticism and backlash from the community. The core of the criticism was the concern that the policy would excessively restrict the use of the Rust trademark for community events, project names, and crate names, thereby stifling the free activities of the ecosystem.17

This controversy led to several important outcomes.

First, the community’s backlash reached a level where the possibility of a language fork named ‘Crab-lang’ was publicly discussed. This was a symbolic event that showed that dissatisfaction with the policy could lead to the possibility of the project splitting.

Second, this incident revealed a difference in communication methods and perception between the Rust Foundation and the developer community that constitutes the project. Criticism was raised that in the process of fulfilling its legal responsibility of trademark protection, the Foundation had not sufficiently considered the open culture and values that the community had long maintained.

As a result, the Rust Foundation accepted the community’s feedback, withdrew the draft policy, and announced its position to redevelop the policy from scratch with the community.18

This case is recorded as an incident that raised important questions about the trust relationship and governance model between the Rust project’s leadership and the community. It provides an important lesson on how an open-source project establishes a formal governance structure and the importance of transparent communication and consensus-building with the community in that process.

8.7 Analysis of the Discourse on Securing Technical Legitimacy by Citing US Government Agency Reports

In the process of arguing for the superiority of a particular technology, the announcements of credible external institutions are often used as important evidence to strengthen the legitimacy of the argument. In the technical discourse related to the Rust language, a pattern is observed where two major reports published by the US National Security Agency (NSA) and the White House are selectively linked and cited. This section will analyze what content each of these two reports contains and how they are combined and interpreted within the technical community to support a particular conclusion.

1. NSA’s Presentation of a List of Memory-Safe Languages (2022-2023)

In November 2022, the U.S. National Security Agency (NSA) released an information report titled “Software Memory Safety.” This report emphasized the importance of ensuring memory safety in software development and recommended a transition to memory-safe languages. In this report, the NSA explicitly listed C#, Go, Java, Ruby, Rust, and Swift as specific examples of memory-safe languages, and later, in an update in April 2023, also included Python, Delphi/Object Pascal, and Ada.19

The release of this report began to be used as important evidence that Rust was mentioned in the same category as other major memory-safe languages by an institution that discusses reliability at the national security level.

2. The White House’s Call for a Transition to Memory-Safe Languages (2024)

In February 2024, the White House Office of the National Cyber Director (ONCD) released a report emphasizing the need for the technology ecosystem to transition to memory-safe languages.20 This report pointed out the serious threat to national cybersecurity posed by vulnerabilities that arise from memory-unsafe languages like C/C++ and urged developers to adopt memory-safe languages by default. The report did not present a specific list of languages but mentioned Rust as ‘an example’ of a memory-safe language.

3. The Formation of Discourse Through the Linkage and Selective Interpretation of the Two Reports

These two reports, due to their differences in content and release timing, have a structural characteristic that allows them to be selectively linked and interpreted to construct a particular logic. That logical construction can take the form of the following step-by-step reasoning.

  1. Premise 1 (NSA Report): A reliable technical institution (NSA) has presented a specific list of memory-safe languages.
  2. Premise 2 (White House Report): The nation’s highest administrative body has declared that the transition to memory-safe languages is an urgent national task.
  3. Inference and Filtering: Based on these two premises, a process of selecting a language that fits the specific purpose of systems programming from the list presented by the NSA proceeds.
    • First, languages that use a garbage collector (GC), such as Python, Java, C#, Go, and Swift, tend to be excluded from the discussion on the grounds that their ‘runtime overhead’ makes them unsuitable for the systems programming domain.
    • Second, in this process, the mention of Ada, one of the non-GC languages included in the NSA’s list, is omitted or not given significant weight.
  4. Drawing a Conclusion: Through this selective filtering, one arrives at the conclusion that “among the safe languages presented by the NSA, Rust is the only realistic alternative that can perform the memory safety tasks of systems programming urged by the White House without a GC.”

This reasoning process is an analytical case study that shows how credible materials with different purposes and contexts can be linked, and how a specific criterion (e.g., ‘absence of GC’) can be selectively applied to derive a conclusion that aligns with the initial premises.

8.8 The Other Side of the Discourse: Official Improvement Efforts and Community Maturity

This chapter has focused on analyzing the patterns of defensive discourse shown by some supporters in response to specific technical criticisms. However, it is necessary to re-emphasize that this phenomenon does not represent the entire picture of the Rust ecosystem. On the contrary, behind this informal discourse, there coexists an official effort to acknowledge Rust’s technical limitations and systematically improve them, which is an even more important indicator for evaluating the health of the ecosystem.

One of the core features of the Rust project is a transparent and open governance model represented by the RFC (Request for Comments) process. Major changes to the language or proposals for new features are publicly discussed through RFC documents that anyone can write. In this process, numerous developers engage in in-depth discussions on technical validity, potential problems, and compatibility with the existing ecosystem, and final decisions are made through this collective intelligence. This is a prime example of a mature culture that develops technology by institutionally accepting constructive criticism rather than avoiding it.

Furthermore, Rust’s core developers and various Working Groups do not evade the several technical challenges pointed out in this book but rather have set them as major improvement goals and are steadily seeking solutions. For example, regarding the complexity and learning curve of the async model, core developers have directly acknowledged the difficulty through their blogs and have presented a long-term vision for improvement, and shortening compile times is one of the top priorities of the compiler team, with continuous research and development being carried out.

In conclusion, to fully understand a technology ecosystem, a balanced perspective that distinguishes between the defensive voices of a few in informal online spaces and the self-critical and constructive improvement efforts made through the project’s official channels is essential. The fact that such a formal and mature feedback loop is strongly operating within the Rust ecosystem is the most important evidence of this technology’s long-term potential and sustainable development possibilities.

9. Re-evaluating Rust: Realistic Strengths, Limitations, and the Developer’s Stance

9.1 Analysis of Rust’s Core Strengths and Key Application Areas

1. Core Strength: Compile-Time Memory Safety Guarantee

One of the most significant technical contributions of the Rust language is the systematic prevention of certain types of memory errors at the language and compiler level. Problems that have long been the cause of major security vulnerabilities in languages like C/C++, such as buffer overflows, use-after-free, and null pointer dereferences, are statically analyzed and blocked at compile time by Rust’s ownership and borrow checker model.

This is a key feature that shifts the paradigm of software safety assurance from ‘error detection and defense at runtime’ to ‘source-level error prevention at compile time.’ If the code successfully compiles, it can be guaranteed with a high level of confidence that these types of memory-related vulnerabilities do not exist.

This memory safety contributes not only to preventing system control hijacking but also to preventing sensitive information leaks. The Heartbleed vulnerability of 2014 is a case that showed how a missing bounds check could lead to a serious information leak. Rust performs bounds checking by default on array and vector access and prohibits access to already freed memory through its ownership system, thereby structurally lowering the possibility of these types of bugs.

In fact, major tech companies like Microsoft and Google have analyzed that about 70% of the serious security vulnerabilities in their products stem from memory safety issues.21 22 This external environment analysis objectively shows why the structural safety guarantee provided by Rust has real and significant value.

2. Key Application Areas: The Intersection of Performance and Stability

Rust’s technical characteristics show high utility in specific industrial fields, and the cloud-native infrastructure and high-performance network services sectors, in particular, are areas where Rust’s core strengths are most effectively demonstrated. This field generally requires consistent low latency without the unpredictable pauses of a garbage collector (GC), and at the same time, requires a high level of security and stability as it is exposed to external attacks.

  • Case Study 1: Discord’s Performance Problem Solving
    Discord, which provides a large-scale voice and text chat service, faced the problem of intermittent latency spikes due to the GC of its service, which was originally written in Go, while handling millions of concurrent users. In real-time communication, such minute delays can be fatal to the user experience. The Discord team rewrote some of the most performance-sensitive backend services (e.g., the ‘Read States’ service) in Rust to solve this problem. As a result, they succeeded in achieving predictable and consistent low latency by eliminating the GC, while also securing memory safety without the risks associated with manual memory management like in C++. This is a representative case that shows that Rust can be an ideal solution to the clear problem of ‘the limitations of GC.’23

  • Case Study 2: Linkerd’s Reliable Proxy Implementation
    The service mesh project Linkerd implemented its data plane proxy (linkerd-proxy), a core component that handles the network traffic of all microservices, in Rust. Since a service mesh is deployed everywhere in the infrastructure, the proxy must be extremely lightweight (low resource footprint), fast, and above all, stable and secure. Rust, through its ‘zero-cost abstraction’ principle, provides performance and low memory usage comparable to C/C++ while also fundamentally reducing the vulnerabilities that can occur in security-sensitive infrastructure components through its compile-time safety guarantees. This proves that Rust is an optimized language for developing ‘system components’ that maximize safety while maintaining the performance of C/C++.24

In addition, many cloud companies like Cloudflare and Amazon Web Services (AWS) are adopting Rust for network services and virtualization technologies (e.g., Firecracker), and Figma is using Rust for high-performance graphics rendering in a WebAssembly environment, clearly demonstrating Rust’s value in specific ‘niche markets.’

3. Market Position and Limitations

In conclusion, Rust has established itself as a powerful solution that overcomes the limitations of existing languages in specific areas where ‘performance’ and ‘safety’ are simultaneously critical, and the presence of a GC is not permissible.

However, this success cannot be immediately extended to all areas of software development.

  • Traditional Systems Programming (C/C++): The vast codebase and ecosystem accumulated over decades in C/C++ in areas like operating systems, embedded systems, and game engines still pose a strong entry barrier.
  • Enterprise Applications (Java/C#): In large-scale enterprise environments, factors like development productivity, a vast library ecosystem, and a stable supply of talent are often more important evaluation criteria than raw runtime performance.

Therefore, Rust’s current position can be evaluated as a success as a ‘specialized tool’ that solves the problems of specific high-value markets, and to become a mainstream general-purpose language, it will need to solve the technical and ecosystem challenges of other areas based on this success.

9.2 The Reality of the Tech Ecosystem and the Developer Competency Model

The technical characteristics of Rust and the current state of its ecosystem offer important implications for the technology choices and competency development strategies of developers who wish to learn and utilize it.

1. An Analysis of the Gap Between Tech Preference Discourse and the Actual Job Market

In surveys like the Stack Overflow Developer Survey, Rust has been selected as the ‘most loved language’ for several years, showing high developer preference. Furthermore, the adoption by major tech companies creates a positive perception of the language’s potential.

However, there is still a difference in scale between this technology preference discourse and the demand in the actual job market. As of 2025, the demand for Rust developers is steadily increasing, but it still occupies a small portion compared to the market size of languages with mature ecosystems like Java, Python, and C++.

This gap, separate from Rust’s technical value, can be interpreted as the result of a combination of several realistic factors that the industry considers when adopting a new technology—namely, the high learning cost, the maturity of the ecosystem in certain fields, and the cost of integration with existing systems, as analyzed in the previous chapters of this book. This suggests that a developer should consider the current market size and ecosystem maturity of a technology, in addition to its popularity or potential, when planning a career.

2. The Relationship Between a Language’s Abstraction Level and Foundational Computer Science Knowledge

Rust’s ownership and lifetimes model demands a deep understanding of the principles of memory management from the developer, which has a positive impact on the cultivation of systems programming capabilities.

However, the high level of abstraction provided by Rust can, paradoxically, limit direct experience with some fundamental computer science principles. For example, since Rust enforces safe memory management at the language level, a developer has fewer opportunities to directly experience and solve errors like memory leaks or double frees that occur during manual memory management (malloc/free) as in C/C++.

Similarly, while using highly optimized standard library data structures like Vec<T> or HashMap<K, V> is convenient, it is a different level of learning from the experience of implementing a linked list or a hash table in a low-level language and dealing with memory layout design or pointer arithmetic.

This shows that learning a specific language cannot cover all the basics of computer science. The experience of direct memory and data structure implementation through a low-level language can be an important foundation for a deeper understanding of the value of the abstractions provided by a high-level, safe language like Rust and their internal workings. Therefore, separate from the mastery of a specific language technology, the importance of universal computer science foundational knowledge, such as data structures, algorithms, and operating systems, remains valid.

9.3 The Culture of a Tech Community and the Sustainability of Its Ecosystem

The long-term success of a particular programming language or technology is deeply related not only to the excellence of the technology itself but also to the culture of the community that surrounds it. The way a community accepts criticism and treats new participants has a significant impact on the health and sustainable development of the ecosystem.

1. The Role of Constructive Criticism and the Feedback Loop

In all technology ecosystems, including open-source projects, external criticism or internal problem-raising can function as an essential feedback mechanism that helps discover system flaws and promote innovation. In particular, technical discussions with language communities that have different design philosophies, such as C++, Ada, and Go, provide an opportunity to view the unique advantages and limitations of a particular technology from multiple angles and discover potential blind spots.

Therefore, how a community accepts and processes this external feedback can be an indicator of the ecosystem’s maturity. As observed in some online discussions, a tendency to perceive technical criticism as a hostile attack and take a defensive stance can deepen technical isolation. In contrast, a culture that takes this as a driving force for growth and integrates it into the official improvement process, like the Rust project’s official RFC process, can increase the credibility of the ecosystem and contribute to its long-term development.

2. The Impact of New Participant Onboarding and Knowledge-Sharing Culture

The sustainability of a technology ecosystem depends heavily on the smooth influx and growth of new participants. In this process, the Rust project officially has a Code of Conduct and aims for an inclusive and friendly community.

However, separate from this official orientation, a contrasting pattern of responding to a beginner’s question is also observed in some informal online technical forums.

  • Exclusionary Communication Style: A style that points out the questioner’s lack of knowledge or effort rather than the content of the question (“Read the official documentation first”), or that denies the premise of the question itself (“Such an approach is not necessary”). This type of interaction can cause the questioner to feel psychologically intimidated, delay problem-solving, and in the long run, lead to a result that discourages the will to participate in the community.

  • Inclusive Communication Style: A style that empathizes with the difficulties the questioner is facing, explains that the cause of the problem may lie in the complexity of the technology itself rather than the individual’s competence, and presents specific information or alternatives for a solution. This type of interaction helps new participants feel psychologically safe and effectively acquire knowledge, and forms a positive perception of the community, laying the foundation for them to grow into potential contributors.

In conclusion, an open attitude that accepts constructive criticism beyond support for a particular technology and a knowledge-sharing culture that embraces new participants are essential elements for a technology ecosystem to move beyond technical maturity to social maturity.

10. Conclusion: Challenges and Prospects for a Sustainable Ecosystem

10.1 Key Challenges for the Qualitative Maturity of the Ecosystem

For Rust to expand its influence as a general-purpose systems programming language beyond its current areas of success, the qualitative maturity of the entire ecosystem emerges as a critical challenge, alongside the language’s technical advantages. This section analyzes the main technical and policy challenges that could affect the sustainable development of the Rust ecosystem in the future.

1. Technical Challenge: The Trade-off Between ABI Stability and Design Philosophy

Currently, Rust does not provide a stable ABI (Application Binary Interface) for its standard library (libstd), which leads most programs to use static linking. This is one of the main causes of increased binary size, which is a constraint for expansion into resource-limited systems.

While this design has the advantage of enabling rapid improvements and optimizations of the language and library, the absence of dynamic linking limits the flexibility of integration with other languages and the potential for use as a system library. Therefore, whether libstd’s ABI will be stabilized in the future will be a crucial technical point of discussion that will show which direction the Rust project will choose between the two values of ‘rapid evolution’ and ‘broad compatibility.’

2. Ecosystem Challenge: Ensuring the Stability and Reliability of Libraries

Rust’s library ecosystem, centered around crates.io, has grown significantly in quantity, but there is room for improvement in its qualitative aspect. Many core libraries are still maintained at versions below 1.0, which implies API instability, and the maintenance model, which relies on the contributions of a few individuals, poses a potential risk to long-term reliability.

To solve these problems, other mature open-source ecosystems utilize the following measures:

  • Financial/Human Support for Core Libraries: Ensuring a stable development environment by supporting the maintenance of core projects through foundation or corporate sponsorship.
  • Introduction of a Maturity Model: Helping users make reliable choices by introducing an official rating system that evaluates the stability, documentation level, and maintenance status of libraries.

These institutional mechanisms can play an important role in helping the Rust ecosystem move beyond quantitative expansion to qualitative maturity.

3. Scalability Challenge: Flexibility for Application in Various Industrial Fields

For Rust to expand beyond its current areas of strength into broader industrial domains, ensuring the flexibility of the language and ecosystem could be a critical challenge.

  • Improving the Usability of the Language and Tools: Efforts to reduce the cognitive load on developers and increase productivity, such as the ‘Polonius’ project to improve the analytical capabilities of the borrow checker, are essential for increasing the accessibility of the language.
  • Considering Various Execution Models: While Rust’s current async model provides high runtime performance based on ‘zero-cost abstraction,’ providing a lightweight thread (Green Thread) model that places more emphasis on development convenience, like Go’s goroutines, as an option, presents a potential to accelerate the adoption of Rust in many network service fields where extreme performance is not required.
  • Strategic Ecosystem Expansion: Strategic library development and the advancement of FFI (Foreign Function Interface) technology in areas where the Rust ecosystem is currently relatively weak, such as desktop GUI and data science, can contribute to broadening the scope of Rust’s application.

These challenges are already being discussed within the Rust community and various Working Groups in the Foundation, and the results will be an important variable in determining Rust’s future standing.

10.2 Synthesis

This book aimed to analyze the core features of the Rust language and its surrounding discourse from multiple perspectives, and to clarify its engineering trade-offs through comparison with other technical alternatives.

The Multilayered Meaning of ‘Safety’ and ‘Performance’

The core values of Rust, ‘safety’ and ‘performance,’ can be considered in a broader engineering context that extends beyond their technical definitions.

  • Expansion of safety: The compile-time memory safety guarantee is a core feature of Rust. The overall reliability of a software system can be expanded to include not only this but also the program’s logical correctness, its resilience to sustain service during errors, and the collaborative environment of the community.
  • Expansion of performance: Rust is designed with a focus on runtime performance optimization. The overall efficiency of a software development project, however, is a comprehensive concept that includes not only runtime performance but also developer productivity, the speed of the development feedback loop (including compile times), and long-term maintenance costs. The balance between runtime performance and other efficiency metrics is a key consideration for the ecosystem.

Analytical Framework for Technology Selection

When evaluating a new technology, the following analytical framework can be applied to systematically review various factors.

  1. Problem Domain: What are the core requirements of the problem you are trying to solve? Is it high runtime performance and low latency (e.g., Rust, C++)? Is it developer productivity and fast time-to-market (e.g., Go, C#)? Or is it a level of reliability that is mathematically provable (e.g., Ada/SPARK)?
  2. Cost Analysis: What are the costs associated with adopting the technology, and can the organization afford them? How should the trade-off between runtime costs (GC) and developer learning costs and compile times be evaluated? Is investment in commercial analysis tools or specialized personnel required?
  3. Ecosystem Maturity: Does the current ecosystem meet the project’s requirements? Are essential libraries stable and reliable? Is the level of official documentation and community support sufficient? Is it easy to source developers with the relevant skills?
  4. Discourse Transparency: Does the technology community openly discuss the technology’s limitations as well as its advantages? In what manner are discussions about external criticism handled? Is an environment fostered that supports new participants in asking questions and learning?

These questions can be utilized to make engineering decisions that best align with one’s realistic constraints and goals by considering various aspects of a technology.

Epilogue

This book has critically analyzed the technical features of the Rust language and the discourse surrounding it from various historical and engineering contexts. The analysis confirms that Rust has achieved a significant technical milestone in the field of systems programming: compile-time memory safety guarantees.

However, at the same time, it was confirmed that Rust’s core design principles—the ownership model, zero-cost abstractions, and error handling through the type system—are the result of a unique integration and enforcement of pre-existing ideas, such as C++’s RAII, the pursuit of safety in Ada/SPARK (used as an analytical tool in this book), and functional programming, and that this process entails clear engineering trade-offs, such as a steep learning curve, long compile times, large binary sizes, and difficulty in implementing certain design patterns.

Furthermore, it was observed that when a dominant narrative emphasizing technical superiority is formed within a particular technical community, this can act as a factor that hinders an objective evaluation of the technology and prevents healthy interaction with other technology ecosystems. Of course, this phenomenon is not the opinion of the entire tech community in question, but a feature that is prominently seen in the discourse of some supporters, who have been the consistent object of analysis in this book. This phenomenon is a pattern also found in other cases in the history of technology, such as the OS wars of the 1990s and 2000s, and can be understood as part of the universal social dynamics that appear when a technical choice is tied to a group’s identity.

In conclusion, the analysis and criticism in this book are not intended to disparage the specific technology of Rust. It is an attempt to be wary of the universal pitfalls of discourse that appear when a technology becomes a ‘social phenomenon.’ Ultimately, all of this discussion is to emphasize together that individual developers must break free from a blind faith in any particular tool, and that the tech community itself must respect and internalize the unchanging essence of engineering: ‘choosing the most appropriate tool for a given problem.’

Appendix: An Analysis of Fallacious Argumentation Observed in Technical Discussions

This appendix analyzes types of unproductive argumentation patterns that can be observed in online technical discussions, intended to aid in understanding the communication styles discussed in the main text. The cases presented here are not for the purpose of criticizing specific individuals or groups, nor are they phenomena confined to any particular technical community. They are examples to explain the universal argumentative fallacies that can appear in any community with a high level of investment in a technology. Each case has been anonymized and aims to analyze specific argument structures and their effects on discussion.

Case 1: Ad Hominem Fallacy

  • Context: When a developer posted a technical critique that Rust’s steep learning curve and the complexity of async could hinder productivity, some users were observed to respond with a pattern of evading the technical points and personally attacking the opponent.
  • Observed Response: “Frankly, your inability to understand async is a problem with your competence, not with Rust. You’re probably not ready to handle complex systems. Consider going back to an easier language.”
  • Analysis: This response, instead of discussing the validity of the technical critique (learning curve, async complexity), questions the competence and qualifications of the individual who made the argument. This corresponds to the ad hominem fallacy, which moves away from the substance of the debate to attack the opponent. Such an argumentation style can act as a factor that hinders the productivity of technical discussions.
  • Socio-Technical Cause Analysis: This type of reaction can be linked to the Rust community’s strong identity around ‘safety’. When memory safety is regarded not merely as a technical feature but as a core value or philosophy of Rust, criticism of key elements that implement safety, such as async or the borrow checker, can be perceived as a challenge to the technology itself. As a result, the discussion shifts from “What is the problem with this feature?” to “Why can’t you understand this important feature?”, creating an environment where it is easy to lead to ad hominem fallacies that shift the subject of criticism from the technology to the individual’s competence.

Case 2: Genetic Fallacy and Circumstantial Fallacy

  • Context: Rust’s borrow checker is a core feature that prevents errors like data races by strictly checking memory access rules at compile time. A C++ expert pointed out that this borrow checker could excessively constrain the flexibility of experienced developers in certain situations. In response to this argument, some users were observed to employ a rhetorical strategy of devaluing the argument by questioning its background or motive, rather than its content.
  • Observed Response: “The reason you feel Rust’s rules are a ‘constraint’ is simply because you’re accustomed to the ‘unsafe’ ways of C++ for decades and are showing ‘resistance’ to a new paradigm. This is a biased view stemming from an attachment to the old ways.”
  • Analysis: This response attempts to devalue the argument by questioning the motive or background for making it (familiarity with C++, fear of change), rather than refuting the content of the argument itself. This can be seen as a form of the genetic fallacy, which evaluates an argument based on its source or motive, and it has the effect of turning a technical point into a psychological analysis.
  • Socio-Technical Cause Analysis: This fallacy is based on the strong ‘C++ alternative narrative’ that shapes the Rust discourse. Within this narrative, C++ is often defined as the ‘unsafe past’. Therefore, criticism from an expert with a C++ background is easily dismissed as a biased perspective of someone accustomed to ‘the old ways’, regardless of its rational content. This creates an environment ripe for the genetic fallacy, which seeks to dismiss an argument by attacking the credibility of its source rather than exploring its technical substance.

Case 3: Straw Man Fallacy

  • Context: When a blog post presented a detailed comparative analysis, such as comparing Rust’s Result type with Java’s ‘checked exceptions’, some users exhibited a pattern of attacking an extremely distorted version of the argument.
  • Observed Response: “So is your argument that Rust’s error handling is ‘useless’? You clearly don’t understand how panic and Result solved the null pointer problem. You just want to do lazy coding by wrapping everything in try...catch.”
  • Analysis: This response distorts the original’s careful comparative analysis (“…has shortcomings compared to…”) into an extreme claim (“is useless”) and then attacks that distorted claim. This corresponds to the straw man fallacy, which attacks an easily assailable, fabricated version of an opponent’s actual argument, making productive discussion impossible.

  1. A system that automatically finds and cleans up memory no longer in use by a program. ↩

  2. Ada and SPARK use formal verification techniques to mathematically prove that certain properties (e.g., absence of runtime errors, logical correctness) hold for all possible execution paths of a program. This is a comprehensive level of stability that goes beyond the memory safety guarantees provided by Rust’s borrow checker and has long been used in fields requiring the highest levels of safety and reliability, such as air traffic control and nuclear power plant control systems. (Source: AdaCore documentation, SPARK User’s Guide, etc.) ↩

  3. The Rustonomicon, “Meet Safe and Unsafe”. “When we say that code is Safe, we are making a promise: this code will not exhibit any Undefined Behavior.” https://doc.rust-lang.org/nomicon/meet-safe-and-unsafe.html ↩

  4. C++ Core Guidelines: A comprehensive set of coding guidelines created and led by Bjarne Stroustrup, the creator of C++, and Herb Sutter. It presents best practices for modern and safe C++ programming, including ownership, resource management, and interface design, and many static analysis tools support the automatic checking of its rules. (See: https://isocpp.github.io/CppCoreGuidelines/) ↩

  5. JetBrains, “The State of Developer Ecosystem 2023,” C++ section. According to the report, while C++17 and C++20 are the most widely used standards, a significant number of projects still use legacy standards from before C++11. ↩

  6. Of course, the Rust standard library provides the std::panic::catch_unwind function, which prevents a thread from immediately terminating when a panic occurs and provides a path to catch it and attempt recovery logic. However, this feature is primarily designed for special purposes, such as handling exceptions at the boundary with external C libraries (FFI) or managing situations where the failure of a specific thread, such as in a thread pool, should not lead to the termination of the entire system. Abusing panics for general application error handling is often considered not to be in line with Rust’s design philosophy. ↩

  7. The complexity of Rust’s async model is recognized as a significant improvement task within the project itself. For example, Jon Gjengset had to explain the concept of Pin in detail in his talk “The Why, What, and How of Pinning in Rust” on his YouTube channel ‘Crust of Rust,’ and core developer Niko Matsakis has also presented related visions and improvement directions several times on his blog. The continuous explanatory efforts of these experts attest to the fact that these concepts are a significant learning hurdle within the Rust community. ↩

  8. The package sizes refer to the ‘Installed size’ provided in the official package database of the Alpine Linux v3.22 stable release. The purpose of this table is not to compare the latest performance at a specific point in time, but to show the structural tendency of how the design method of each language ecosystem affects binary size. This fundamental tendency is not significantly influenced by minor patch updates or version changes that may occur within a stable release, so a specific stable release was chosen as a standard for reproducibility of the data and consistency of the argument. The versions of each referenced package are as specified in the table. ↩

  9. The analysis was performed by unarchiving linux-6.15.5.tar.xz and then running the command cloc . without any additional options in the root directory of the source code. This information is provided to allow the reader to verify the analysis results directly using the same method. ↩

  10. The term ‘silver bullet narrative’ used in this text is not intended to disparage any particular technology or community, but is an analytical term widely used in the sociology of technology. It refers to the tendency to believe that there is a single, overly simplified, perfect technical solution to a complex problem, and it shares a context with ‘technological triumphalism.’ This term is used to more objectively describe the structure of the discourse in question. ↩ ↩2

  11. The discourse analysis conducted in Part 4 does not target specific individuals or private communities. Its basis lies in the qualitative observation of recurring argumentation patterns found in publicly accessible information, such as public discussions on major online platforms like X (formerly Twitter), Hacker News, and Reddit (e.g., r/rust, r/programming); numerous tech blog posts on the theme of “Why Rust?”; and Q&A sessions at related tech conferences. The purpose of this analysis is not to measure the statistical frequency of this discourse but to critically understand its structure and logic. ↩

  12. ‘M$’ was a derogatory term used in some Linux and open-source communities in the 1990s to criticize the commercial policies of Microsoft. It mockingly conveyed the company’s commercialism by replacing the ‘S’ in ‘Microsoft’ with the dollar sign ($), which symbolizes money (M$, Micro$oft). ↩

  13. RTFM is an abbreviation for ‘Read The Fucking Manual,’ an informal and rude expression meaning ‘just read the damn manual.’ It was often used as a term that shows the exclusive side of the 1990s hacker culture, berating users who ask basic questions to find the answers themselves. ↩

  14. This method of evaluating the value of an argument based on its source or motive, rather than its content, corresponds to the ‘genetic fallacy.’ (See Appendix, ‘Case 2: Genetic Fallacy’) ↩

  15. Questioning the competence or qualities of the individual who made the claim, rather than the validity of the criticism raised, corresponds to the ‘ad hominem fallacy.’ (See Appendix, ‘Case 1: Ad Hominem Fallacy’) ↩

  16. No True Scotsman Fallacy: A logical fallacy named by British philosopher Antony Flew. For instance, after claiming “No Scotsman puts sugar on his porridge,” one is faced with a rebuttal like, “But my Scottish friend does.” The response is to amend the claim to, “Well, no true Scotsman does.” It refers to the attempt to evade a counterargument by redefining the subject with an arbitrary standard like ‘true.’ ↩

  17. Thomas Claburn, “Rust Foundation apologizes for bungled trademark policy”, The Register, April 17, 2023. https://www.theregister.com/2023/04/17/rust_foundation_apologizes_trademark_policy/ ↩

  18. Rust Foundation, “Rust Trademark Policy Draft Revision & Next Steps,” Rust Foundation Blog, April 11, 2023. https://rustfoundation.org/media/rust-trademark-policy-draft-revision-next-steps/ ↩

  19. National Security Agency, “Software Memory Safety,” CSI-001-22, November 2022. https://media.defense.gov/2022/Nov/10/2003112742/-1/-1/0/CSI_SOFTWARE_MEMORY_SAFETY.PDF ↩

  20. Office of the National Cyber Director, “Back to the Building Blocks: A Path Toward Secure and Measurable Software,” February 2024. https://bidenwhitehouse.archives.gov/wp-content/uploads/2024/02/Final-ONCD-Technical-Report.pdf ↩

  21. Microsoft Security Response Center, “A Proactive Approach to More Secure Code”, 2019-07-16. https://msrc.microsoft.com/blog/2019/07/16/a-proactive-approach-to-more-secure-code/ ↩

  22. Google has emphasized the importance of memory safety in several projects.
    Chrome: “The Chromium project finds that around 70% of our serious security bugs are memory safety problems.”, The Chromium Projects, “Memory-Safe Languages in Chrome”, https://www.chromium.org/Home/chromium-security/memory-safety/ (This page is continuously updated)
    Android: “Memory safety bugs are a top cause of stability issues, and consistently represent ~70% of Android’s high severity security vulnerabilities.”, Google Security Blog, “Memory Safe Languages in Android 13”, 2022-12-01. https://security.googleblog.com/2022/12/memory-safe-languages-in-android-13.html ↩

  23. Discord Engineering, “Why Discord is switching from Go to Rust”, 2020-02-04. https://discord.com/blog/why-discord-is-switching-from-go-to-rust ↩

  24. Linkerd, “Under the Hood of Linkerd’s Magic”, Linkerd Docs. https://linkerd.io/2/reference/architecture/#proxy ↩