Demystifying Faulty Code with LLM: Step-by-Step Reasoning for Explainable Fault Localization
Ratnadira Widyasari, Jia Wei Ang, Truong Giang Nguyen, Neil Sharma, David Lo
TL;DR
This study investigates the step-by-step reasoning for explainable fault localization and proposes FuseFL, a proposed FuseFL that utilizes several combinations of information to enhance the LLM results which are spectrum-based fault localization results, test case execution outcomes, and code description.
Abstract
Fault localization is a critical process that involves identifying specific program elements responsible for program failures. Manually pinpointing these elements, such as classes, methods, or statements, which are associated with a fault is laborious and time-consuming. To overcome this challenge, various fault localization tools have been developed. These tools typically generate a ranked list of suspicious program elements. However, this information alone is insufficient. A prior study emphasized that automated fault localization should offer a rationale. In this study, we investigate the step-by-step reasoning for explainable fault localization. We explore the potential of Large Language Models (LLM) in assisting developers in reasoning about code. We proposed FuseFL that utilizes several combinations of information to enhance the LLM results which are spectrum-based fault localization results, test case execution outcomes, and code description (i.e., explanation of what the given code is intended to do). We conducted our investigation using faulty code from Refactory dataset. First, we evaluate the performance of the automated fault localization. Our results demonstrate a more than 30% increase in the number of successfully localized faults at Top-1 compared to the baseline. To evaluate the explanations generated by FuseFL, we create a dataset of human explanations that provide step-by-step reasoning as to why specific lines of code are considered faulty. This dataset consists of 324 faulty code files, along with explanations for 600 faulty lines. Furthermore, we also conducted human studies to evaluate the explanations. We found that for 22 out of the 30 randomly sampled cases, FuseFL generated correct explanations.