An Investigation of Ensemble Approaches to Cross-Version Defect Prediction

Software defect prediction can help software testers to focus on software modules with more defects. Many ensemble methods have been proposed for software defect prediction to divide software modules into defect-prone and defect-free, and these ensemble methods have been proved to be more effective than single learning algorithms. A few ensemble approaches have been applied to predict the number of defects in software modules, and they also perform well in most cases. The good performance of ensemble approaches implies that ensemble algorithms might not only improve the accuracy of software defect classification models, but also improve the performance of defect ranking models. Therefore, we propose an ensemble method based on Yang et al.'s learning-to-rank approach in this paper. Experimental results show that the learning-to-rank-based ensemble approach performs better than the single learningto-rank approach, which means that the idea of ensemble can improve the performance of the learning-to-rank approach to sort modules in order of defect count. We also conduct a comparison study of ensemble approaches for cross-version defect prediction over 30 sets of cross-version data, which indicates that the ensemble technique of random subspace is more appropriate than boosting over these experimental data sets.