Evaluating the Predictive Power of Software Metrics for Fault Localization

Fault localization remains a critical challenge in software engineering, directly impacting debugging efficiency and software quality. This study investigates the predictive power of various software metrics for fault localization by framing the task as a multi-class classification problem and evaluating it using the Defects4J dataset. We fitted thousands of models and benchmarked different algorithms—including deep learning, Random Forest, XGBoost, and LightGBM—to choose the best-performing model. To enhance model transparency, we applied explainable AI techniques to analyze feature importance. The results revealed that test suite metrics consistently outperform static and dynamic metrics, making them the most effective predictors for identifying faulty classes. These findings underscore the critical role of test quality and coverage in automated fault localization. By combining machine learning with transparent feature analysis, this work delivers practical insights to support more efficient debugging workflows. It lays the groundwork for an iterative process that integrates metric-based predictive models with large language models (LLMs), enabling future systems to automatically generate targeted test cases for the most fault-prone components, which further enhances the automation and precision of software testing.