Enhancing IR-based Fault Localization using Large Language Models

Abstract: Information Retrieval-based Fault Localization (IRFL) techniques aim to identify source files containing the root causes of reported failures. While existing techniques excel in ranking source files, challenges persist in bug report analysis and query construction, leading to potential information loss. Leveraging LLMs like GPT-4, this paper enhances IRFL by categorizing bug reports based on programming entities, stack traces, and natural language text. Tailored query strategies, the initial step in our approach (LLmiRQ), are applied to each category. To address inaccuracies in queries, we introduce a user and conversational-based query reformulation approach, termed LLmiRQ+. Additionally, to further enhance query utilization, we implement a learning-to-rank model that leverages key features such as class name match score and call graph score. This approach significantly improves the relevance and accuracy of queries. Evaluation on 46 projects with 6,340 bug reports yields an MRR of 0.6770 and MAP of 0.5118, surpassing seven state-of-the-art IRFL techniques, showcasing superior performance.