On the Role of Fault Localization Context for LLM-Based Program Repair
Fault Localization (FL) is a key component of Large Language Model (LLM)-based Automated Program Repair (APR), yet its impact remains underexplored. In particular, it is unclear how much localization is needed, whether additional context beyond the predicted buggy location is beneficial, and how such context should be retrieved. We conduct a large-scale empirical study on 500 SWE-bench Verified instances using GPT-5-mini, evaluating 61 configurations that vary file-level, element-level, and line-level context. Our results show that more context does not consistently improve repair performance. File-level localization is the dominant factor, yielding a 15-17x improvement over a no-file bas...
| Authors | Melika Sepidband, Hung Viet Pham, Hadi Hemmati |
|---|---|
| Primary category | cs.SE |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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