# HAFixAgent: History-Aware Program Repair Agent

arXiv: https://arxiv.org/abs/2511.01047 · HTML: https://mlsanity.com/paper/2511.01047/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 16 of its 83 references that are in the map.
- First posted on arXiv 2 Nov 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 16 of 16)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [DeepSeek-V3 Technical Report](https://mlsanity.com/paper/2412.19437/) (2024)
- [SWE-Exp: Experience-Driven Software Issue Resolution](https://mlsanity.com/paper/2507.23361/) (2025)
- [Agentic Software Engineering: Foundational Pillars and a Research Roadmap](https://mlsanity.com/paper/2509.06216/) (2025)
- [A Systematic Literature Review on Large Language Models for Automated Program Repair](https://mlsanity.com/paper/2405.01466/) (2024)
- [UniDebugger: Hierarchical Multi-Agent Framework for Unified Software Debugging](https://mlsanity.com/paper/2404.17153/) (2024)
- [EXPEREPAIR: Dual-Memory Enhanced LLM-based Repository-Level Program Repair](https://mlsanity.com/paper/2506.10484/) (2025)
- [An Empirical Study on LLM-based Agents for Automated Bug Fixing](https://mlsanity.com/paper/2411.10213/) (2024)
- [SWE-Effi: Re-Evaluating Software AI Agent System Effectiveness Under Resource Constraints](https://mlsanity.com/paper/2509.09853/) (2025)
- [Towards Effectively Leveraging Execution Traces for Program Repair with Code LLMs](https://mlsanity.com/paper/2505.04441/) (2025)
- [Agentic AI for Software: thoughts from Software Engineering community](https://mlsanity.com/paper/2508.17343/) (2025)
- [A Multi-Agent Approach to Fault Localization via Graph-Based Retrieval and Reflexion](https://mlsanity.com/paper/2409.13642/) (2024)
- [The Rise of AI Teammates in Software Engineering (SE) 3.0: How Autonomous Coding Agents Are Reshaping Software Engineering](https://mlsanity.com/paper/2507.15003/) (2025)
- [Characterizing Multi-Hunk Patches: Divergence, Proximity, and LLM Repair Challenges](https://mlsanity.com/paper/2506.04418/) (2025)
- [Hierarchical Knowledge Injection for Improving LLM-based Program Repair](https://mlsanity.com/paper/2506.24015/) (2025)
- [HAFix: History-Augmented Large Language Models for Bug Fixing](https://mlsanity.com/paper/2501.09135/) (2025)

## Built on by (most cited first, 3 of 3)

- [AtomicCommitBench: Can Coding Agents Reconstruct Commit Histories from Squashed Patches?](https://mlsanity.com/paper/2607.03332/) (2026)
- [RepoRescue: An Empirical Study of LLM Agents on Whole-Repository Compatibility Rescue](https://mlsanity.com/paper/2607.01213/) (2026)
- [Better Understanding, Better Fixes? A Study of Hallucination in LLM-based Automated Program Repair](https://mlsanity.com/paper/2609.04909/) (2026)

## Abstract (arXiv)

Automated program repair (APR) has recently shifted toward large language models and agent-based systems, while prior work shows that repository history can help repair single-line bugs. We conduct a large-scale empirical study of whether, how much, and under what conditions repository history improves agentic APR beyond this setting. We present HAFixAgent, a history-aware program repair agent that injects blame-derived repository context into its repair loop. Across 854 Defects4J (Java) and 501 BugsInPy (Python) bugs, we first show that bug-relevant history is widely available. We then evaluate HAFixAgent across three base models under perfect fault localization. The best history heuristic improves over non-history from +4.4% on Defects4J and +1.1% on BugsInPy with the strongest model to as much as +64.3% with weaker models. HAFixAgent also outperforms RepairAgent by +56.6% and BIRCH-feedback by +47.1% on the strongest model, showing that it is a strong repair agent independent of history. The three history heuristics are complementary: each repairs a different subset of bugs, so their hybrid significantly repairs more bugs than non-history (McNemar's test, p < 0.001), and this advantage persists under realistic spectrum-based fault localization. Individual history heuristics are generally no more expensive than non-history in agent steps or tokens under either localization setting, while the hybrid costs roughly twice a single run in a cascade setting.