Performance evaluation of ensemble methods for software fault prediction: An experiment

Shahid Hussain, Jacky Keung, Arif Ali Khan, Kwabena Ebo Bennin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationASWEC 2015 - 24th Australasian Software Engineering Conference
EditorsFei-Ching Kuo, Haifeng Shen, M. Ali Babar, Stuart Marshall, Markus Stumptner
PublisherAssociation for Computing Machinery
Number of pages5
ISBN (Electronic)9781450337960
StatePublished - Sep 28 2015
Event24th Australasian Software Engineering Conference, ASWEC 2015 - Adelaide, Australia
Duration: Sep 28 2015Oct 1 2015

Publication series

NameACM International Conference Proceeding Series


Conference24th Australasian Software Engineering Conference, ASWEC 2015

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


Dive into the research topics of 'Performance evaluation of ensemble methods for software fault prediction: An experiment'. Together they form a unique fingerprint.

Cite this