Xinyu Shi, Zhou Yang, An Ran Chen · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.25826 · cited by 0 works (OpenAlex) · open-access PDF
오류 잠재적 인 작업 패트 로컬링 (FL)은 소프트웨어 실패에 책임있는 코드 위치를 식별하는 것을 목표로하고 일반적으로 알려진 오류 위치를 정확하게 평가합니다. 대형 언어 모델 (LLM) 에이전트는 프로그램 수리를 변경하고 점점 더 보안에 대한 검색 및 이유를 사용합니다. 최근 LLM 기반 FL 방법은 일반적인 수리 프레임 워크 내에서 3 개의 LLM을 개선했지만, 에이전트 수리를위한 그들의 영향은 여전히 명확하지 않습니다.이 격차를 해결하기 위해, 우리는 에이전트 수리를위한 FL의 체계적인 실험 평가를 수행합니다. 우리는 SWE-bench Verified 및 Defects4J에 대한 6 개의 최근 FL 방법을 평가합니다. SWE-bench Verified 및 Defects4J에 대한
Machine translation (Meta M2M100); the English original follows.
Fault localization (FL) aims to identify code locations responsible for software failures and is commonly evaluated by how accurately it ranks known fault locations. Large language model (LLM) agents are changing program repair and are increasingly used to search and reason over repositories. Although recent LLM-based FL methods have improved localization accuracy, their impact on agentic repair remains unclear. To address this gap, we conduct a systematic empirical study of FL in agentic repair. We evaluate six recent FL methods on SWE-bench Verified and Defects4J with three LLMs under a common repair framework and analyze 16,248 repair trajectories. We also examine gold locations and bug-cause descriptions as forms of FL guidance across different repair contexts. The studied FL methods do not consistently improve repair success, and higher localization accuracy does not consistently lead to larger repair gains. Yet FL guides agents' early search and helps them reach relevant code sooner. Accurate locations can reduce repair token use by up to 39.2\%, but current FL methods often consume more tokens than they save during repair. Experiments with gold guidance suggest that FL has greater potential when bug inputs contain fewer location clues. Providing bug causes can further improve repair success beyond locations alone. We also find that ground truth from a single reference patch does not fully capture the locations used by successful repairs. These findings help clarify the value of FL in agentic repair and inform future work on its design and evaluation.
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Topics: Computer science, Code (set theory), Reliability engineering, Fault (geology), Software, Empirical research
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