Work

Towards Explorative IRBL: Combining Semantic Retrieval with LLM-Driven Iterative Code Exploration

Year: 2026

Type: article

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. In contrast, recent Large Language Model (LLM)-based approaches struggle to provide appropriate context to the model: they either restrict analysis to a fixed set of candidate files, overwhelm the model with repository-wide information, or rely on explicit bug report cues to guide context collection. To address these issues, we propose GenLoc, a technique that combines semantic retrieval with LLM-driven code-exploration functions to iteratively analyze the code base and identify buggy files. We evaluate GenLoc on three complementary benchmarks, including large-scale and recent Java datasets as well as the Python based SWE-bench Lite dataset. Results demonstrate that GenLoc substantially outperforms traditional IRBL, deep learning-based approaches and recent LLM-based methods, while also localizing bugs that other techniques fail to detect.

Source: Proceedings of the ACM on software engineering.

DOI: 10.1145/3832151

Authors: Moumita Asad, Rafed Muhammad Yasir, Sam Malek

Institution: University of California, Irvine

Language: English

Topic: Software Engineering Research

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: National Science Foundation

Awards: Collaborative Research: SHF: Medium: A General Framework for Automated Test Transfer, SHF: Medium: Automated Software Engineering Techniques for Improving the Accessibility of Software