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

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)

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Detail(s)

Original languageEnglish
Title of host publicationACM International Conference Proceeding Series
PublisherAssociation for Computing Machinery
Pages91-95
Volume28-September-2015
ISBN (Print)9781450337960
Publication statusPublished - 28 Sep 2015

Publication series

Name
Volume28-September-2015

Conference

Title24th Australasian Software Engineering Conference, ASWEC 2015
PlaceAustralia
CityAdelaide
Period28 September - 1 October 2015

Abstract

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.

Research Area(s)

  • Chidamber and Kemerer (CK) Metrics, Classifiers, Ensemble Methods, Measures, Performance, Weka

Citation Format(s)

Performance evaluation of ensemble methods for software fault prediction : An experiment. / Hussain, Shahid; Keung, Jacky; Khan, Arif Ali; Bennin, Kwabena Ebo.

ACM International Conference Proceeding Series. Vol. 28-September-2015 Association for Computing Machinery, 2015. p. 91-95.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)