Performance analysis of ensemble learning for predicting defects in open source software

Machine learning techniques have been earnestly explored by many software engineering researchers. At present state of art, there is no conclusive evidence on the kind of machine learning techniques which are most accurate and efficient for software defect prediction but some recent studies suggest that combining multiple machine learners, that is, ensemble learning, may be a more accurate alternative. This study contributes to software defect prediction literature by systematically evaluating the predictive accuracy of three well known homogeneous ensemble methods - Bagging, Boosting, and Rotation Forest, utilizing fifteen important underlying base learners, by exploiting the data of nine open source object-oriented systems obtained from the PROMISE repository. Results indicate while Bagging and Boosting may result in AUC performance loss, AUC performance improvement results in twelve of the fifteen investigated base learners with Rotation Forest ensemble.