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arXiv · 2511.01047

HAFixAgent: History-Aware Program Repair Agent

Yu Shi·Hao Li·Bram Adams·Ahmed E. Hassan
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Abstract

Automated program repair (APR) has recently shifted toward large language models and agent-based systems, yet most systems rely on local snapshot context, overlooking repository history. Prior work shows that repository history helps repair single-line bugs, since the last commit touching the buggy line is often the bug-introducing one. In this paper, we investigate whether repository history can also improve agentic APR systems at scale, especially for complex multi-hunk bugs. We present HAFixAgent, a History-Aware Bug-Fixing Agent that injects blame-derived repository heuristics into its repair loop. A preliminary study on 854 Defects4J (Java) and 501 BugsInPy (Python) bugs motivates our design, showing that bug-relevant history is widely available across both benchmarks. Using the same LLM (DeepSeek-V3.2-Exp) for all experiments, including replicated baselines, we show: (1) Effectiveness: HAFixAgent outperforms RepairAgent (+56.6\%) and BIRCH-feedback (+47.1\%) on Defects4J. Historical context further improves repair by +4.4\% on Defects4J and +38.6\% on BugsInPy, especially on single-file multi-hunk (SFMH) bugs. (2) Robustness: under noisy fault localization (+1/+3/+5 line shifts), history provides increasing resilience, maintaining 40 to 56\% success on SFMH bugs where the non-history baseline collapses to 0\%. (3) Efficiency: history does not significantly increase agent steps or token costs on either benchmark.

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Yu Shi, Hao Li, Bram Adams, Ahmed E. Hassan. 2026-04-01. HAFixAgent: History-Aware Program Repair Agent. https://arxiv.org/abs/2511.01047

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