A Multi-Agent Approach to Fault Localization via Graph-Based Retrieval and Reflexion (2409.13642v2)

Abstract: Identifying and resolving software faults remains a challenging and resource-intensive process. Traditional fault localization techniques, such as Spectrum-Based Fault Localization (SBFL), leverage statistical analysis of test coverage but often suffer from limited accuracy. While learning-based approaches improve fault localization, they demand extensive training datasets and high computational resources. Recent advances in LLMs offer new opportunities by enhancing code understanding and reasoning. However, existing LLM-based fault localization techniques face significant challenges, including token limitations, performance degradation with long inputs, and scalability issues in complex software systems. To overcome these obstacles, we propose LLM4FL, a multi-agent fault localization framework that utilizes three specialized LLM agents. First, the Context Extraction Agent applies an order-sensitive segmentation strategy to partition large coverage data within the LLM's token limit, analyze failure context, and prioritize failure-related methods. The Debugger Agent then processes the extracted data, which employs graph-based retrieval-augmented code navigation to reason about failure causes and rank suspicious methods. Finally, the Reviewer Agent re-evaluates the identified faulty methods using verbal reinforcement learning, engaging in self-criticism and iterative refinement. Evaluated on the Defects4J (V2.0.0) benchmark, which includes 675 faults from 14 Java projects, LLM4FL achieves an 18.55\% improvement in Top-1 accuracy over AutoFL and 4.82\% over SoapFL. It outperforms supervised techniques such as DeepFL and Grace, all without requiring task-specific training. Furthermore, its coverage segmentation and prompt chaining strategies enhance performance, increasing Top-1 accuracy by up to 22\%.