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arXiv AI By Anik Jha

Does Fault Localization Beat a Fresh Attempt? A Placebo-Controlled Study of Test-Guided Code Repair

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The study evaluates whether fault localization improves test‑guided code repair by comparing three approaches—blind whole‑solution resampling, spectrum‑based localized infilling, and random‑span infilling—across multiple large language models and benchmarks. Results show that localization is rarely available (only 9.0% of failing candidates), and when it is, localized infilling performs worse than blind resampling, with only suggestive evidence of a benefit over random spans. The findings suggest that targeted edits may not provide a consistent advantage over broader, untargeted repair attempts in current large‑model settings.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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