Enhancing LLM-based Fault Localization with a Functionality-Aware Retrieval-Augmented Generation Framework

Fault localization (FL) is a critical but time-consuming task in software debugging, aiming to identify faulty code elements. While recent advances in large language models (LLMs) have shown promise for FL, they often struggle with complex systems due to the lack of project-specific knowledge and the difficulty of navigating large projects. To address these limitations, we propose FaR-Loc, a novel framework that enhances method-level FL by integrating LLMs with retrieval-augmented generation (RAG). FaR-Loc consists of three key components: LLM Functionality Extraction, Semantic Dense Retrieval, and LLM Re-ranking. First, given a failed test and its associated stack trace, the LLM Functionality Extraction module generates a concise natural language description that captures the failing behavior. Next, the Semantic Dense Retrieval component leverages a pre-trained code-understanding encoder to embed both the functionality description (natural language) and the covered methods (code) into a shared semantic space, enabling the retrieval of methods with similar functional behavior. Finally, the LLM Re-ranking module reorders the retrieved methods based on their contextual relevance. Our experiments on the widely used Defects4J benchmark show that FaR-Loc outperforms state-of-the-art LLM-based baselines SoapFL and AutoFL, by 14.6% and 9.1% in Top-1 accuracy, by 19.2% and 22.1% in Top-5 accuracy, respectively. It also surpasses all learning-based and spectrum-based baselines across all Top-N metrics without requiring re-training. Furthermore, we find that pre-trained code embedding models that incorporate code structure, such as UniXcoder, can significantly improve fault localization performance by up to 49.0% in Top-1 accuracy. Finally, we conduct a case study to illustrate the effectiveness of FaR-Loc and to provide insights for its practical application.