WGNCS: A robust hybrid cross-version defect model via multi-objective optimization and deep enhanced feature representation

Abstract

For a constantly-evolving software project with multiple releasing versions, Cross-Version Defect Prediction (CVDP) can identify the potential defect in the latter one by mining historical defect information from the prior releasing software versions. Unfortunately, the imbalanced class distribution and the complex intrinsic structure in software projects make it challenging to obtain suitable defect features and construct a predominant CVDP model. To address these challenges, we propose a robust hybrid CVDP model named WGNCS based on WGAN-GP (Wasserstein GAN with Gradient Penalty), multi-objective NSGA-III (Non-dominated Sorting Genetic Algorithm - III) algorithm and hybrid CNN-SVM (Convolutional Neural Network – Support Vector Machine) in this study, which has three main merits: (1) employ a powerful deep learning generative model – WGAN-GP to conduct data augmentation tasks, thereby achieving defect class balance and generating more training instances. (2) utilize the multi-objective NSGA-III algorithm to select the fewest representative training feature subset for the minimum error. (3) construct a single powerful defect predictor CNN-SVM by cascading a high-level deep semantic feature extractor (CNN) and a classifier (SVM) with the fixed non-linear Gaussian kernel function. We verify the CVDP performance of WGNCS on 32 cross-version pairs derived from 45 software project versions. The experimental results demonstrate that the WGNCS model can exhibit encouraging performance.