AI builds, We Analyze: An Empirical Study of AI-Generated Build Code Quality

The rapid adoption of AI coding agents for software development has raised important questions about the quality and maintainability of the code they produce. While prior studies have examined AI-generated source code, the impact of AI coding agents on build systems-a critical yet understudied component of the software lifecycle-remains largely unexplored. This data mining challenge focuses on AIDev, the first large-scale, openly available dataset capturing agent-authored pull requests (Agentic-PRs) from real-world GitHub repositories. Our paper leverages this dataset to investigate (RQ1) whether AI coding agents generate build code with quality issues (e.g., code smells), (RQ2) to what extent AI agents can eliminate code smells from build code, and (RQ3) to what extent Agentic-PRs are accepted by developers. We identified 364 maintainability and security-related build smells across varying severity levels, indicating that AI-generated build code can introduce quality issues-such as lack of error handling, and hardcoded paths or URLs-while also, in some cases, removing existing smells through refactorings (e.g., Pull Up Module and Externalize Properties). Notably, more than 61% of Agentic-PRs are approved and merged with minimal human intervention. This dual impact underscores the need for future research on AI-aware build code quality assessment to systematically evaluate, guide, and govern AI-generated build systems code.