An empirical study of bagging and boosting ensembles for identifying faulty classes in object-oriented software

Identifying faulty classes in object-oriented software is one of the important software quality assurance activities. This paper empirically investigates the application of two popular ensemble techniques (bagging and boosting) in identifying faulty classes in object-oriented software, and evaluates the extent to which these ensemble techniques offer an increase in classification accuracy over single classifiers. As base classifiers, we used multilayer perceptron, radial basis function network, bayesian belief network, naïve bayes, support vector machines, and decision tree. The experiment was based on well-known and respected NASA dataset. The results indicate that bagging and boosting yield improved classification accuracy over most of the investigated single classifiers. In some cases, bagging outperforms boosting, while in some other cases, boosting outperforms bagging. However, in case of support vector machines, neither bagging nor boosting improved its classification accuracy.