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Improving Performance in Software Defect Prediction Using Variational Autoencoder

Software defect prediction (SDP) is a beneficial task to save limited resources in the software testing stage for improving software quality. However, the imbalanced distribution in defect datasets could be a challenge for often machine learning algorithms, an effect on the performance of the algorithms. To overcome this issue, oversampling techniques from the minority class has been adopted. In this work, we suggest a new oversampling method, which trained a variational autoencoder (VAE) to generate synthesized samples aimed for output mimicked minority samples that were then combined with training dataset into an augmented training dataset. In the experiments, we explored ten SDP datasets from the PROMISE freely accessible repository. We measured the performance of the proposed method by comparing it with state-of-the-art oversampling techniques including Random Over-Sampling, SMOTE, Borderline-SMOTE, and ADASYN. Based on the investigation results, the proposed method provides better mean performance of SDP models between all examined techniques.