{"paper":{"title":"Large Language Models for Fault Localization: An Empirical Study","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Anquan Jie, Boyang Yang, Hongwei Li, Jianjun Huang, Rongqun Hu, Weiwei Gong, Yingjian Xiao","submitted_at":"2025-10-23T13:04:22Z","abstract_excerpt":"Large Language Models (LLMs) have demonstrated strong performance on code-related tasks, particularly in automated program repair. However, repair effectiveness often depends on accurate upstream fault localization, while the statement-level fault localization capability of LLMs remains insufficiently evaluated. This paper presents a systematic empirical study of LLMs for statement-level fault localization. We evaluate four representative LLMs, including two open-weight models, Qwen2.5-Coder-32B-Instruct and DeepSeek-V3, and two closed-source models, GPT-4.1 mini and Gemini-2.5-Flash, on Human"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.20521","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2510.20521/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}