Software defect prediction based on classifiers ensemble

Software defect prediction using classification algorithms was advocated by many researchers. However, several new literatures show the performance bottleneck by applying a single classifier recent years. On the other hand, classifiers ensemble can effectively improve classification performance than a single classifier. Motivated by above two reasons which indicate that defect prediction using classifiers ensemble methods have not fully be exploited, we conduct a comparative study of various ensemble methods with perspective of taxonomy. These methods included Bagging, Boosting, Random trees, Random forest, Random subspace, Stacking, and Voting. We also compared these ensemble methods to a single classifier Naive Bayes. A series of benchmarking experiments on public-domain datasets MDP show that applying classifiers ensemble methods to predict defect could achieve better performance than using a single classifier. Specially, in all seven ensemble methods evolved by our experiments, Voting and Random forest had obvious performance superiority than others, and Stacking also had better generalization ability.