RGFL: Reasoning Guided Fault Localization for Automated Program Repair Using Large Language Models

• URL: http://arxiv.org/abs/2601.18044v1
• Date: Sun, 25 Jan 2026 23:41:42 GMT
• Title: RGFL: Reasoning Guided Fault Localization for Automated Program Repair Using Large Language Models
• Authors: Melika Sepidband, Hamed Taherkhani, Hung Viet Pham, Hadi Hemmati,
• Abstract summary: We present a novel project-level FL approach that improves both file- and element-level localization.<n>We evaluate our approach on Python and Java projects from SWE-bench Verified, Lite, and Java.
• Score: 1.9196411948992402
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fault Localization (FL) is a critical step in Automated Program Repair (APR), and its importance has increased with the rise of Large Language Model (LLM)-based repair agents. In realistic project-level repair scenarios, software repositories often span millions of tokens, far exceeding current LLM context limits. Consequently, models must first identify a small, relevant subset of code, making accurate FL essential for effective repair. We present a novel project-level FL approach that improves both file- and element-level localization. Our method introduces a hierarchical reasoning module that (i) generates structured, bug-specific explanations for candidate files and elements, and (ii) leverages these explanations in a two-stage ranking scheme combining LLM-based and embedding-based signals. We further propose a counterfactual upper-bound analysis to quantify the contribution of each localization stage to repair success. We evaluate our approach on Python and Java projects from SWE-bench Verified, Lite, and Java. Compared to state-of-the-art baselines, including Agentless and OpenHands, our method consistently improves localization accuracy. On SWE-bench Verified, file-level Hit@1 improves from 71.4% to 85%, and MRR from 81.8% to 88.8%. At the element level, Exact Match under top-3 files increases from 36% to 69%. Integrating our localization into Agentless yields a 12.8% end-to-end repair success improvement.

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