Can AI Write Its Own Build Systems?

Author: Denis Avetisyan

A new study examines the quality of code generated by AI to manage software builds, revealing both improvements and potential pitfalls.

The proliferation of code smells-indications of deeper problems within the software-reveals a predictable pattern of entropy as AI agents contribute to the system, suggesting that even ostensibly intelligent automation accelerates the accumulation of technical debt rather than alleviating it.

Researchers conducted an empirical analysis of build code created by AI coding agents, finding they can introduce and resolve code smells with surprisingly high developer acceptance rates.

While the increasing use of AI coding agents promises to accelerate software development, a critical gap remains in understanding the quality of the build systems code they generate. Our study, ‘AI builds, We Analyze: An Empirical Study of AI-Generated Build Code Quality’, addresses this by empirically investigating AI-authored pull requests from real-world GitHub repositories, revealing that these agents can both introduce and remediate code smells within build configurations. Specifically, we identified 364 maintainability and security-related smells, alongside a surprisingly high acceptance rate of over 61% for AI-generated changes with minimal human review. This dual impact raises the question of how we can systematically assess and govern AI’s contribution to build code quality, ensuring robust and maintainable software systems.

The Inevitable Entropy of Build Systems

Contemporary software development frequently depends on intricate build systems – automated processes that transform source code into executable applications. However, these systems, while essential, are often subject to the accumulation of technical debt. Initially designed for efficiency, build processes can become complex and unwieldy over time due to evolving project requirements, inconsistent tooling, and a lack of dedicated maintenance. This often manifests as convoluted scripting, duplicated logic, and fragile dependencies. The resulting technical debt isn’t directly visible in the application’s code, but it silently erodes development velocity, increases build times, and ultimately hinders the ability to rapidly iterate and deliver new features – a hidden cost frequently underestimated in software projects.

The foundation of any software project – its build system – frequently harbors subtle indicators of deeper problems, manifesting as ‘code smells’. These aren’t bugs, but rather structural deficiencies within the build process itself – overly complex scripts, duplicated logic, or reliance on outdated tools – that gradually erode maintainability. A poorly maintained build not only extends compilation times but also obscures dependencies, making refactoring risky and hindering the ability to quickly adapt to new requirements. Furthermore, these smells create opportunities for security vulnerabilities; neglected build tools may contain known exploits, and convoluted build processes can inadvertently introduce malicious code or bypass security checks. Ultimately, a build system riddled with code smells transforms from a facilitator of development into a significant source of technical debt, increasing long-term costs and jeopardizing the overall health of the software.

Unaddressed code smells within build systems escalate development costs through a compounding effect. Initially manifesting as minor inefficiencies, these issues-such as overly complex dependencies or duplicated logic-demand increasing developer time for debugging and modification as the project evolves. This technical debt isn’t merely a matter of slowed velocity; neglected build system smells frequently create security vulnerabilities. Poorly maintained build processes can inadvertently introduce exploitable paths, improperly handle sensitive data, or fail to adequately validate external components. The cumulative impact extends beyond immediate fixes, requiring costly refactoring, security audits, and potentially, emergency patching to mitigate risks – ultimately diverting resources from innovation and feature development.

Unearthing the Hidden Flaws: Common Build Configuration Smells

Maintainability smells within build configurations, specifically the use of wildcard characters and reliance on deprecated dependencies, introduce significant risks to build reliability and reproducibility. Wildcard usage in dependency declarations can pull in unintended and potentially conflicting versions of libraries, leading to inconsistent builds across different environments or over time. Similarly, dependencies marked as deprecated are no longer actively maintained, meaning bug fixes and security patches are unlikely, increasing the chance of build failures or introducing vulnerabilities. These practices create implicit and often undocumented dependencies, hindering the ability to reliably recreate builds and increasing maintenance overhead as projects evolve.

Insufficient error handling in build processes frequently results in silent failures, where build systems continue execution despite encountering issues such as missing dependencies, failed tests, or compilation errors. These failures are not immediately reported, potentially leading to the creation of faulty artifacts and undetected regressions. The absence of explicit error checks and appropriate logging makes diagnosing the root cause of build problems significantly more difficult and time-consuming. Specifically, build scripts often lack checks for non-zero exit codes from critical commands, or fail to propagate errors upwards, obscuring the true status of the build and delaying identification of underlying problems within the codebase or infrastructure.

Hardcoded credentials within build configurations represent a significant security vulnerability impacting the entire software supply chain. These credentials, such as passwords, API keys, or private tokens, directly embedded in build scripts or configuration files, are exposed during the build process and can be accessed by anyone with access to the build environment or resulting artifacts. This exposure allows malicious actors to compromise systems, data, and intellectual property. The risk is compounded by the frequent storage of these credentials in version control systems, further expanding the attack surface. Best practices mandate the use of secure credential management systems, environment variables, or dedicated secrets management tools to avoid storing sensitive information directly within build configurations.

Refactoring Towards Resilience: Strategies for Robust Build Systems

Refactoring build configurations addresses the technical debt accumulated over time, improving the long-term health of the build system. This process involves restructuring existing build files – such as Makefiles, Gradle scripts, or Maven POMs – without altering external functionality. Common refactoring targets include eliminating duplication, simplifying complex logic, and improving readability. Proactive refactoring reduces the cognitive load for developers maintaining the build, decreases the risk of introducing errors during modifications, and facilitates easier onboarding of new team members. Neglecting refactoring leads to brittle build systems that are difficult to debug, extend, and adapt to changing project requirements, ultimately increasing maintenance costs and slowing down development cycles.

Externalizing properties involves moving configuration values – such as file paths, database connection strings, and API keys – out of the build script and into dedicated configuration files or environment variables. This practice enhances portability, security, and ease of modification without requiring changes to the core build logic. Conversely, the “Pull Up Module” technique addresses code duplication by identifying and extracting common build logic into reusable modules or functions. This reduces redundancy, improves maintainability, and simplifies updates, as changes need only be applied in the central module rather than repeated across multiple build configurations. Both techniques contribute to a more modular, flexible, and maintainable build system.

Automated build system analysis tools, commonly referred to as “Sniffers,” operate by parsing build configuration files – such as those written in Make, CMake, or Gradle – and applying a defined set of rules to identify common code smells and anti-patterns. These tools can detect issues like hardcoded paths, duplicated logic, inconsistent naming conventions, and unnecessary complexity. Beyond detection, some Sniffers offer automated remediation suggestions, including code transformations or refactoring proposals, significantly reducing the manual effort required to improve build system quality. The speed of analysis, often completing in seconds for large projects, allows for continuous integration pipelines to incorporate build health checks as a standard practice, preventing the accumulation of technical debt and promoting maintainability.

The Inevitable Automation: AI and the Future of Build Systems

Artificial intelligence is rapidly transforming software development workflows, and build systems are proving to be a fertile ground for automation. AI coding agents are now capable of not only generating initial build configurations, but also intelligently modifying existing ones to improve efficiency and maintainability. These agents leverage machine learning techniques to understand complex build processes and proactively address potential issues, reducing the need for manual intervention. This capability extends beyond simple scripting; agents can analyze code, identify inefficiencies, and suggest optimized build pipelines, leading to faster build times and more reliable software releases. The potential for significant automation in build systems promises to free up valuable developer time, allowing engineers to focus on innovation rather than repetitive configuration tasks.

Artificial intelligence agents are demonstrating a capacity to automate build system configuration by learning from extensive datasets, such as the AIDev Dataset, which contains a wealth of best practices. This data-driven approach enables these agents to not only generate build configurations but also proactively identify and address code smells-indicators of potential maintainability issues-within those configurations. Remarkably, a significant proportion-61.4%-of pull requests containing AI-generated build changes are successfully merged, suggesting a high degree of compatibility and usefulness within existing codebases. This automation promises to reduce manual effort, accelerate development cycles, and improve the overall quality of build processes through the consistent application of learned best practices.

A detailed analysis of build configuration changes reveals a high degree of stability and improvement facilitated by AI agents. Of the 945 files modified, a substantial 824 exhibited no discernible code smells either before or after the alterations, suggesting the AI primarily refactors without introducing new issues. Critically, 31 files demonstrated genuine quality enhancement, with a total of 54 code smells successfully removed. This positive impact on code quality wasn’t merely subjective; the assessment benefited from strong inter-rater reliability, as indicated by a Fleiss’ Kappa score of 0.76. This score signifies substantial agreement among multiple labelers when identifying files with demonstrable improvements, bolstering confidence in the AI’s ability to not only automate build processes, but also contribute to cleaner, more maintainable configurations.

Despite the promising automation offered by AI-driven build system agents, realizing their full potential necessitates comprehensive testing and validation protocols. While agents demonstrate an ability to modify configurations and even remediate code smells, ensuring the reliability and correctness of these changes remains paramount. This often requires careful scrutiny beyond automated checks, potentially involving manual inspection of generated configurations by human experts. Such review allows for the identification of subtle errors or unintended consequences that automated systems might overlook, ultimately safeguarding the integrity and stability of the build process and the software it produces. Rigorous validation, therefore, is not merely a precautionary step, but a critical component in deploying these powerful AI agents effectively.

The study reveals a curious paradox: AI, in its attempt to automate build processes, simultaneously introduces and resolves code smells. This echoes a fundamental truth about complex systems – they aren’t built, they evolve. Every architectural decision, even those driven by artificial intelligence, carries the seed of future complications. As Linus Torvalds observed, “Talk is cheap. Show me the code.” The acceptance of most AI-generated build changes with minimal review suggests a pragmatic tolerance for imperfection; developers aren’t seeking flawless builds, but functional ecosystems. The focus shifts from preventing all smells to managing their inevitable proliferation, a recognition that order is merely a fleeting illusion within the larger chaos of software maintenance.

What’s Next?

The observation that AI agents alter build code, introducing and resolving what humans label ‘smells,’ isn’t a demonstration of progress-it’s a glimpse into the inevitable. Systems don’t fail; they evolve. Long stability in build processes isn’t a sign of robust engineering, but a symptom of a hidden inflexibility-a brittle core awaiting disruption. This work highlights not the capabilities of these agents, but the precariousness of assuming any system will remain static. The metric isn’t whether the AI ‘improves’ code, but whether the changes are acceptable-a crucial distinction suggesting adaptation, not optimization, is the true goal.

Future inquiry shouldn’t focus on quantifying ‘quality’-a phantom variable-but on charting the drift in these build ecosystems. What unforeseen dependencies are seeded with each automated change? How does the surface acceptance of these modifications mask deeper architectural erosion? The real question isn’t whether AI can write build code, but whether humans can understand the systems these agents are growing.

The current emphasis on isolated code smells is shortsighted. These are merely surface manifestations of deeper systemic pressures. The next phase demands tools that model the entire build lifecycle-not as a sequence of tasks, but as a complex, evolving organism. The goal isn’t to control the system, but to observe its transformations, and to learn from the shapes it inevitably takes.

Original article: https://arxiv.org/pdf/2601.16839.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

The Inevitable Entropy of Build Systems

Unearthing the Hidden Flaws: Common Build Configuration Smells

Refactoring Towards Resilience: Strategies for Robust Build Systems

The Inevitable Automation: AI and the Future of Build Systems

What’s Next?

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