Understanding Automated Program Repair Agents Through the Lens of Traceability: An Empirical Study 文章

arXiv:2506.08311v2 Announce Type: replace-cross Abstract: Automated Program Repair (APR) agents leverage Large Language Models (LLMs) to autonomously diagnose and fix software bugs through reasoning, planning, and tool use. Despite impressive leaderboard gains on benchmarks such as SWE-bench, little is understood about how these agents take actions, where they fail, and how their behavior compares to that of human developers. This paper presents the first systematic analysis of five state-of-the-art APR agents across 500 real-world repair tasks, tracing their full decision-making pipelines -- from issue description to patch validation. Our study reveals that while agents excel at simple fixes, they struggle with logic-intensive bugs, often producing verbose or overfitted patches that merely satisfy existing tests. We find that test generation and regression test selection remain major bottlenecks, with agents frequently failing to reproduce issues or run relevant regression tests.