Defect prediction using deep learning with Network Portrait Divergence for software evolution

Vijay Walunj, Gharib Gharibi, Rakan Alanazi, Yugyung Lee

Abstract: Understanding software evolution is essential for software development tasks, including debugging, maintenance, and testing. As a software system evolves, it grows in size and becomes more complex, hindering its comprehension. Researchers proposed several approaches for software quality analysis based on software metrics. One of the primary practices is predicting defects across software components in the codebase to improve agile product quality. While several software metrics exist, graph-based metrics have rarely been utilized in software quality. In this paper, we explore recent network comparison advancements to characterize software evolution and focus on aiding software metrics analysis and defect prediction. We support our approach with an automated tool named GraphEvoDef. Particularly, GraphEvoDef provides three major contributions: (1) detecting and visualizing significant events in software evolution using call graphs, (2) extracting metrics that are suitable for software comprehension, and (3) detecting and estimating the number of defects in a given code entity (e.g., class). One of our major findings is the usefulness of the Network Portrait Divergence metric, borrowed from the information theory domain, to aid the understanding of software evolution. To validate our approach, we examined 29 different open-source Java projects from GitHub and then demonstrated the proposed approach using 9 use cases with defect data from the the PROMISE dataset. We also trained and evaluated defect prediction models for both classification and regression tasks. Our proposed technique has an 18% reduction in the mean square error and a 48% increase in squared correlation coefficient over the state-of-the-art approaches in the defect prediction domain.