Leveraging Large Language Model for Information Retrieval-based Bug Localization

• URL: http://arxiv.org/abs/2508.00253v2
• Date: Tue, 07 Oct 2025 03:00:42 GMT
• Title: Leveraging Large Language Model for Information Retrieval-based Bug Localization
• Authors: Moumita Asad, Rafed Muhammad Yasir, Sam Malek,
• Abstract summary: GenLoc combines semantic retrieval with code-exploration functions to identify potential buggy files.<n>We evaluate GenLoc on two diverse datasets: a benchmark of 9,097 bugs from six large open-source projects and the GHRB dataset of 131 recent bugs across 16 projects.
• Score: 5.556612138252782
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Information Retrieval-based Bug Localization (IRBL) aims to identify buggy source files for a given bug report. Traditional and deep-learning-based IRBL techniques often suffer from vocabulary mismatch and dependence on project-specific metadata, while recent Large Language Model (LLM)-based approaches are limited by insufficient contextual information. To address these issues, we propose GenLoc, an LLM-based technique that combines semantic retrieval with code-exploration functions to iteratively analyze the code base and identify potential buggy files. We evaluate GenLoc on two diverse datasets: a benchmark of 9,097 bugs from six large open-source projects and the GHRB (GitHub Recent Bugs) dataset of 131 recent bugs across 16 projects. Results demonstrate that GenLoc substantially outperforms traditional IRBL, deep learning approaches and recent LLM-based methods, while also localizing bugs that other techniques fail to detect.

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