A compositional model for effort-aware Just-In-Time defect prediction on android apps

Android apps have played important roles in daily life and work. To meet the new requirements from users, the apps encounter frequent updates, which involves a large quantity of code commits. Previous studies proposed to apply Just-in-Time (JIT) defect prediction for apps to timely identify whether the new code commits can introduce defects into apps, aiming to assure their quality. In general, high-quality features are benefits for improving the classification performance. In addition, the number of defective commit instances is much fewer than that of clean ones, that is the defect data is class imbalanced. In this study, a novel compositional model, called KPIDL, is proposed to conduct the JIT defect prediction task for Android apps. More specifically, KPIDL first exploits a feature learning technique to preprocess original data for obtaining better feature representation, and then introduces a state-of-the-art cost-sensitive cross-entropy loss function into the deep neural network to alleviate the class imbalance issue by considering the prior probability of the two types of classes. The experiments were conducted on a benchmark defect data consisting of 15 Android apps. The experimental results show that the proposed KPIDL model performs significantly better than 25 comparative methods in terms of two effort-aware performance indicators in most cases.