HAFixAgent: History-Aware Program Repair Agent

TL;DR

HAFixAgent leverages repository history to enhance large language model-based automated program repair, significantly improving success rates, robustness, and efficiency, especially for complex multi-hunk bugs.

Contribution

This work introduces HAFixAgent, a novel history-aware repair agent that injects repository heuristics into the repair process, improving performance over existing methods.

Findings

HAFixAgent outperforms RepairAgent (+56.6%) and BIRCH-feedback (+47.1%) on Defects4J.

Historical context improves repair success by +4.4% on Defects4J and +38.6% on BugsInPy.

History increases resilience under noisy fault localization, maintaining 40-56% success on complex bugs.

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.