Understanding the impact of code and process metrics on post-release defects

Abstract

Research studying the quality of software applications continues to grow rapidly with researchers building regression models that combine a large number of metrics. However, these models are hard to deploy in practice due to the cost associated with collecting all the needed metrics, the complexity of the models and the black box nature of the models. For example, techniques such as PCA merge a large number of metrics into composite metrics that are no longer easy to explain. In this paper, we use a statistical approach recently proposed by Cataldo et al. to create explainable regression models. A case study on the Eclipse open source project shows that only 4 out of the 34 code and process metrics impacts the likelihood of finding a post-release defect. In addition, our approach is able to quantify the impact of these metrics on the likelihood of finding post-release defects. Finally, we demonstrate that our simple models achieve comparable performance over more complex PCA-based models while providing practitioners with intuitive explanations for its predictions.