RGFL: Reasoning Guided Fault Localization for Automated Program Repair Using Large Language Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917223425114112 |
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| author | Sepidband, Melika Taherkhani, Hamed Pham, Hung Viet Hemmati, Hadi |
| author_facet | Sepidband, Melika Taherkhani, Hamed Pham, Hung Viet Hemmati, Hadi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_18044 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RGFL: Reasoning Guided Fault Localization for Automated Program Repair Using Large Language Models Sepidband, Melika Taherkhani, Hamed Pham, Hung Viet Hemmati, Hadi Software Engineering 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. |
| title | RGFL: Reasoning Guided Fault Localization for Automated Program Repair Using Large Language Models |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2601.18044 |