PhoenixRepair: Rethinking Repair Strategy Exploration in Software Agents

2026-07-21

AI Summary

Existing agent-based automated program repair approaches are limited in both the breadth of edit locations explored and the depth of repair attempts. This work proposes PhoenixRepair, a multi-agent framework that systematically expands the search space and enhances repair quality by integrating graph-guided fault localization to broaden candidate location exploration, alongside iterative reflection and repair knowledge distillation to deepen repair reasoning. PhoenixRepair is the first method to jointly achieve extensive coverage and in-depth repair attempts while effectively leveraging large language models. Evaluated on SWE-bench-Verified, it outperforms SWE-agent by 7.8% and achieves a Pass@1 repair rate of 76.0% on MiniMax-M2.5, with significantly improved fault localization accuracy over current state-of-the-art methods.

Abstract

While Large Language Models have greatly advanced automated issue resolution, existing agent-based methods exhibit a fundamental limitation in their insufficient exploration of repair strategies. This insufficiency manifests in two key aspects. First, the exploration of multiple potential edit locations is limited. Second, the exploration of repair attempts at each location is also insufficient. To address these challenges, we present PhoenixRepair, a multi-agent framework that systematically explores multiple candidate edit locations and performs iterative reflection and refinement on patch generation, thereby expanding the search space of repair strategies. Our framework begins with multi-location sampling, optionally augmented with graph-based localization information for difficult tasks, followed by iterative reflection and refinement to generate better patches, culminating in final-round generation guided by distilled insights from all historical attempts. Experiments on SWE-bench-Verified demonstrate that PhoenixRepair achieves the largest relative improvement of 7.8\% over SWE-agent under DeepSeek-V3.1, and attains the highest resolved rate of 76.0\% Pass@1 under MiniMax-M2.5. Meanwhile, it achieves higher fault localization accuracy than existing approaches. Our code is available at https://github.com/DeepSoftwareAnalytics/PhoenixRepair.

Problem

• repair strategy exploration
• automated program repair
• software agents
• fault localization
• patch generation

Innovation

• multi-agent framework
• repair strategy exploration
• iterative reflection
• fault localization
• patch generation