TY - GEN
T1 - Performance evaluation of ensemble methods for software fault prediction
T2 - 24th Australasian Software Engineering Conference, ASWEC 2015
AU - Hussain, Shahid
AU - Keung, Jacky
AU - Khan, Arif Ali
AU - Bennin, Kwabena Ebo
N1 - Publisher Copyright:
© 2015 ACM.
PY - 2015/9/28
Y1 - 2015/9/28
N2 - In object-oriented software development, a plethora of studies have been carried out to present the application of machine learning algorithms for fault prediction. Furthermore, it has been empirically validated that an ensemble method can improve classification performance as compared to a single classifier. But, due to the inherent differences among machine learning and data mining approaches, the classification performance of ensemble methods will be varied. In this study, we investigated and evaluated the performance of different ensemble methods with itself and base-level classifiers, in predicting the faults proneness classes. Subsequently, we used three ensemble methods AdaboostM1, Vote and StackingC with five base-level classifiers namely Naivebayes, Logistic, J48, VotedPerceptron and SMO in Weka tool. In order to evaluate the performance of ensemble methods, we retrieved twelve datasets of open source projects from PROMISE repository. In this experiment, we used k-fold (k=10) cross-validation and ROC analysis for validation. Besides, we used recall, precision, accuracy, F-value measures to evaluate the performance of ensemble methods and base-level Classifiers. Finally, we observed significant performance improvement of applying ensemble methods as compared to its base-level classifier, and among ensemble methods we observed StackingC outperformed other selected ensemble methods for software fault prediction.
AB - In object-oriented software development, a plethora of studies have been carried out to present the application of machine learning algorithms for fault prediction. Furthermore, it has been empirically validated that an ensemble method can improve classification performance as compared to a single classifier. But, due to the inherent differences among machine learning and data mining approaches, the classification performance of ensemble methods will be varied. In this study, we investigated and evaluated the performance of different ensemble methods with itself and base-level classifiers, in predicting the faults proneness classes. Subsequently, we used three ensemble methods AdaboostM1, Vote and StackingC with five base-level classifiers namely Naivebayes, Logistic, J48, VotedPerceptron and SMO in Weka tool. In order to evaluate the performance of ensemble methods, we retrieved twelve datasets of open source projects from PROMISE repository. In this experiment, we used k-fold (k=10) cross-validation and ROC analysis for validation. Besides, we used recall, precision, accuracy, F-value measures to evaluate the performance of ensemble methods and base-level Classifiers. Finally, we observed significant performance improvement of applying ensemble methods as compared to its base-level classifier, and among ensemble methods we observed StackingC outperformed other selected ensemble methods for software fault prediction.
UR - http://www.scopus.com/inward/record.url?scp=84958766163&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84958766163&partnerID=8YFLogxK
U2 - 10.1145/2811681.2811699
DO - 10.1145/2811681.2811699
M3 - Conference contribution
AN - SCOPUS:84958766163
T3 - ACM International Conference Proceeding Series
SP - 91
EP - 95
BT - ASWEC 2015 - 24th Australasian Software Engineering Conference
A2 - Kuo, Fei-Ching
A2 - Shen, Haifeng
A2 - Ali Babar, M.
A2 - Marshall, Stuart
A2 - Stumptner, Markus
PB - Association for Computing Machinery
Y2 - 28 September 2015 through 1 October 2015
ER -