QiMeng-PRepair: Precise Code Repair via Edit-Aware Reward Optimization
Large Language Models (LLMs) achieve strong program repair performance but often suffer from over-editing, where excessive modifications overwrite correct code and hinder bug localization. We systematically quantify its impact and introduce precise repair task, which maximizes reuse of correct code while fixing only buggy parts. Building on this insight, we propose PRepair, a framework that mitigates over-editing and improves repair accuracy. PRepair has two components: Self-Breaking, which generates diverse buggy programs via controlled bug injection and min-max sampling, and Self-Repairing, which trains models with Edit-Aware Group Relative Policy Optimization (EA-GRPO) using an edit-aw...
| Authors | Changxin Ke, Rui Zhang, Jiaming Guo et al. |
|---|---|
| Primary category | cs.SE |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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