Capturing Contextual Relationships of Buggy Classes for Detecting Quality-Related Bugs

Quality concerns are critical for addressing system-wide issues related to reliability, security, and performance, among others. However, these concerns often become scattered across the codebase, making it challenging for software developers to effectively address quality bugs. In this paper, we propose a holistic approach to detecting and clustering quality-related content hidden within the codebase. By leveraging the Hierarchical Dirichlet Process (HDP) and complementary techniques such as information retrieval and machine learning, including structural and textual analysis, we create a meaningful hierarchy that detects classes containing relevant information for addressing quality bugs. This approach allows us to uncover rich synergies between complex structured artifacts and infer bug-fixing classes for repairing quality bugs. The reported results show that our approach improves over the state-of-the-art achieving a high precision of 83%, recall of 82%, and F1 score of 83%.