TY - GEN

T1 - Building an Ensemble for Software Defect Prediction Based on Diversity Selection

AU - Petrić, Jean

AU - Bowes, David

AU - Hall, Tracy

AU - Christianson, Bruce

AU - Baddoo, Nathan

N1 - © ACM, 2016. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ESEM '16 Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement http://dx.doi.org/10.1145/2961111.2962610

PY - 2016/9/8

Y1 - 2016/9/8

N2 - Background: Ensemble techniques have gained attention in various scientific fields. Defect prediction researchers have investigated many state-of-the-art ensemble models and concluded that in many cases these outperform standard single classifier techniques. Almost all previous work using ensemble techniques in defect prediction rely on the majority voting scheme for combining prediction outputs, and on the implicit diversity among single classifiers. Aim: Investigate whether defect prediction can be improved using an explicit diversity technique with stacking ensemble, given the fact that different classifiers identify different sets of defects. Method: We used classifiers from four different families and the weighted accuracy diversity (WAD) technique to exploit diversity amongst classifiers. To combine individual predictions, we used the stacking ensemble technique. We used state-of-the-art knowledge in software defect prediction to build our ensemble models, and tested their prediction abilities against 8 publicly available data sets. Conclusion: The results show performance improvement using stacking ensembles compared to other defect prediction models. Diversity amongst classifiers used for building ensembles is essential to achieving these performance improvements.

AB - Background: Ensemble techniques have gained attention in various scientific fields. Defect prediction researchers have investigated many state-of-the-art ensemble models and concluded that in many cases these outperform standard single classifier techniques. Almost all previous work using ensemble techniques in defect prediction rely on the majority voting scheme for combining prediction outputs, and on the implicit diversity among single classifiers. Aim: Investigate whether defect prediction can be improved using an explicit diversity technique with stacking ensemble, given the fact that different classifiers identify different sets of defects. Method: We used classifiers from four different families and the weighted accuracy diversity (WAD) technique to exploit diversity amongst classifiers. To combine individual predictions, we used the stacking ensemble technique. We used state-of-the-art knowledge in software defect prediction to build our ensemble models, and tested their prediction abilities against 8 publicly available data sets. Conclusion: The results show performance improvement using stacking ensembles compared to other defect prediction models. Diversity amongst classifiers used for building ensembles is essential to achieving these performance improvements.

KW - diversity

KW - ensembles of learning machines

KW - Software defect prediction

KW - software faults

KW - stacking

U2 - 10.1145/2961111.2962610

DO - 10.1145/2961111.2962610

M3 - Conference contribution/Paper

AN - SCOPUS:84991666877

BT - ESEM '16 Proceedings of the 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement

PB - Association for Computing Machinery, Inc

CY - New York

T2 - 10th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2016

Y2 - 8 September 2016 through 9 September 2016

ER -