We are honored to have a paper accepted to the 32nd International Conference on the Foundations of Software Engineering, titled [A Quantitative and Qualitative Evaluation of LLM-based Explainable Fault Localization]. As many are aware of, large language models (LLMs) are showing strong performance in many different domains, but had not yet been applied to fault localization, as providing an entire repository to a large language model is generally infeasible. In this work, we tackle this challenge by providing LLMs with tools so that it can autonomously navigate the repository and find relevant code within the framework of our tool AutoFL.

Overall, we find that AutoFL could substantially outperform existing fault localization techniques, all while only using failing tests unlike other approaches which require more resources:

If you are interested, you can find many more details in our preprint!