A Change-Level Defect Prediction Approach based on Teacher–Student Network

Change-level defect prediction, also known as just-in-time (JIT) defect prediction, concentrates on predicting if a specific commit is likely to introduce defects. It effectively alleviates the limitations of traditional file-level defect prediction techniques, such as coarse-grained, hard to trace and poor timeliness. Currently, most change-level defect prediction techniques construct defect prediction models by using either expert features or semantic features. Recent studies have shown that the defect prediction performance can be enhanced by integrating these two types of features. However, obtaining expert features is not an easy task, due to missing historical data in real projects. To address the aforementioned problem, this paper proposes TS-SDP (Teacher–Student based Software Defect Prediction) based on teacher–student network. First, the source code is analyzed to extract expert features and semantic features. Then, a teacher–student network framework is constructed. In this framework, both features are used as inputs to the teacher network and only semantic features are used as inputs to the student network. The student network is enabled to learn about the expert features from the teacher network through the loss function. Finally, the student network is used to differentiate commits that are defect-inducing and those that are not, in the presence of only semantic features. The results of the experiments carried out on a dataset containing 21 different projects show that, when only semantic features are available, the cross-network knowledge dissemination between the teacher and student network makes it possible to predict defects. When compared to the state-of-the-art change-level defect prediction method, JIT-Fine, TS-SDP is 0.130, 0.114, 0.123 and 0.016 greater in [Formula: see text], [Formula: see text], F-measure and [Formula: see text], respectively.