Adaptive selection of classifiers for bug prediction: A large-scale empirical analysis of its performances and a benchmark study

Bug prediction aims at locating defective source code components relying on machine learning models. Although some previous work showed that selecting the machine-learning classifier is crucial, the results are contrasting. Therefore, several ensemble techniques, i.e., approaches able to mix the output of different classifiers, have been proposed. In this paper, we present a benchmark study in which we compare the performance of seven ensemble techniques on 21 open-source software projects. Our aim is twofold. On the one hand, we aim at bridging the limitations of previous empirical studies that compared the accuracy of ensemble approaches in bug prediction. On the other hand, our goal is to verify how ensemble techniques perform in different settings such as cross- and local-project defect prediction. Our empirical experimentation results show that ensemble techniques are not a silver bullet for bug prediction. In within-project bug prediction, using ensemble techniques improves the prediction performance with respect to the best stand-alone classifier. We confirm that the models based on VALIDATION AND VOTING achieve slightly better results. However, they are similar to those obtained by other ensemble techniques. Identifying buggy classes using external sources of information is still an open problem. In this setting, the use of ensemble techniques does not provide evident benefits with respect to stand-alone classifiers. The statistical analysis highlights that local and global models are mostly equivalent in terms of performance. Only one ensemble technique (i.e., ASCI) slightly exploits local learning to improve performance.