Extracting Conceptual Knowledge to Locate Software Issues

• URL: http://arxiv.org/abs/2509.21427v2
• Date: Sat, 04 Oct 2025 15:20:21 GMT
• Title: Extracting Conceptual Knowledge to Locate Software Issues
• Authors: Ying Wang, Wenjun Mao, Chong Wang, Zhenhao Zhou, Yicheng Zhou, Wenyun Zhao, Yiling Lou, Xin Peng,
• Abstract summary: RepoLens is a novel approach that abstracts and leverages conceptual knowledge from code repositories.<n>It operates in two stages: an offline stage that extracts conceptual knowledge into a repository-wide knowledge base, and an online stage that retrieves issue-specific terms.<n>RepoLens consistently improves three state-of-the-art tools, achieving average gains of over 22% in Hit@k and 46% in Recall@k for file- and function-level localization.
• Score: 12.746044344302623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Issue localization, which identifies faulty code elements such as files or functions, is critical for effective bug fixing. While recent LLM-based and LLM-agent-based approaches improve accuracy, they struggle in large-scale repositories due to concern tangling, where relevant logic is buried in large functions, and concern scattering, where related logic is dispersed across files. To address these challenges, we propose RepoLens, a novel approach that abstracts and leverages conceptual knowledge from code repositories. RepoLens decomposes fine-grained functionalities and recomposes them into high-level concerns, semantically coherent clusters of functionalities that guide LLMs. It operates in two stages: an offline stage that extracts and enriches conceptual knowledge into a repository-wide knowledge base, and an online stage that retrieves issue-specific terms, clusters and ranks concerns by relevance, and integrates them into localization workflows via minimally intrusive prompt enhancements. We evaluate RepoLens on SWE-Lancer-Loc, a benchmark of 216 tasks derived from SWE-Lancer. RepoLens consistently improves three state-of-the-art tools, namely AgentLess, OpenHands, and mini-SWE-agent, achieving average gains of over 22% in Hit@k and 46% in Recall@k for file- and function-level localization. It generalizes across models (GPT-4o, GPT-4o-mini, GPT-4.1) with Hit@1 and Recall@10 gains up to 504% and 376%, respectively. Ablation studies and manual evaluation confirm the effectiveness and reliability of the constructed concerns.

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