Software defect prediction using ensemble learning: A systematic literature review

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dc.contributor.authorMatloob, Faseeha
dc.contributor.authorGhazal, Taher M.
dc.contributor.authorTaleb, Nasser
dc.contributor.authorAftab, Shabib
dc.contributor.authorAhmad, Munir
dc.contributor.authorKhan, Muhammad Adnan
dc.date.accessioned2021-08-10T13:45:00Z
dc.date.available2021-08-10T13:45:00Z
dc.date.copyright© 2013
dc.date.issued2021
dc.descriptionThis review is not available at CUD collection. The version of scholarly record of this review is published in IEEE Access (2021), available online at: https://doi.org/10.1109/ACCESS.2021.3095559en_US
dc.description.abstractRecent advances in the domain of software defect prediction (SDP) include the integration of multiple classification techniques to create an ensemble or hybrid approach. This technique was introduced to improve the prediction performance by overcoming the limitations of any single classification technique. This research provides a systematic literature review on the use of the ensemble learning approach for software defect prediction. The review is conducted after critically analyzing research papers published since 2012 in four well-known online libraries: ACM, IEEE, Springer Link, and Science Direct. In this study, five research questions covering the different aspects of research progress on the use of ensemble learning for software defect prediction are addressed. To extract the answers to identified questions, 46 most relevant papers are shortlisted after a thorough systematic research process. This study will provide compact information regarding the latest trends and advances in ensemble learning for software defect prediction and provide a baseline for future innovations and further reviews. Through our study, we discovered that frequently employed ensemble methods by researchers are the random forest, boosting, and bagging. Less frequently employed methods include stacking, voting and Extra Trees. Researchers proposed many promising frameworks, such as EMKCA, SMOTE-Ensemble, MKEL, SDAEsTSE, TLEL, and LRCR, using ensemble learning methods. The AUC, accuracy, F-measure, Recall, Precision, and MCC were mostly utilized to measure the prediction performance of models. WEKA was widely adopted as a platform for machine learning. Many researchers showed through empirical analysis that features selection, and data sampling was necessary pre-processing steps that improve the performance of ensemble classifiers. © 2013 IEEE.en_US
dc.identifier.citationMatloob, F., Ghazal, T. M., Taleb, N., Aftab, S., Ahmad, M., Khan, M. A., . . . Soomro, T. R. (2021). Software defect prediction using ensemble learning: A systematic literature review. IEEE Access, 9, 98754-98771. https://doi.org/10.1109/ACCESS.2021.3095559en_US
dc.identifier.issn21693536
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2021.3095559
dc.identifier.urihttp://hdl.handle.net/20.500.12519/421
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relationAuthors Affiliations : Matloob, F., Department of Computer Science, Virtual University of Pakistan, Lahore, 44000, Pakistan; Ghazal, T.M., Faculty of Information Science and Technology, Center for Cyber Security, Universiti Kebangsaan Malaysia, Bangi, Selangor, 43600, Malaysia, School of Information Technology, Skyline University College, University City of Sharjah, Sharjah, United Arab Emirates; Taleb, N., Faculty of Management, Canadian University Dubai, Dubai, United Arab Emirates; Aftab, S., Department of Computer Science, Virtual University of Pakistan, Lahore, 44000, Pakistan, School of Computer Science, National College of Business Administration and Economics, Lahore, 54660, Pakistan; Ahmad, M., School of Computer Science, National College of Business Administration and Economics, Lahore, 54660, Pakistan; Khan, M.A., Pattern Recognition and Machine Learning Laboratory, Department of Software, Gachon University, Seongnam, 13557, South Korea; Abbas, S., School of Computer Science, National College of Business Administration and Economics, Lahore, 54660, Pakistan; Soomro, T.R., CCSIS, Institute of Business Management, Sindh, Karachi, 75190, Pakistan
dc.relation.ispartofseriesIEEE Access;Volume 9
dc.rightsCreative Common Attribution 4.0 International (CC BY 4.0) License
dc.rights.holder© 2013 IEEE.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectEnsemble classifieren_US
dc.subjectHybrid classifieren_US
dc.subjectSoftware defect predictionen_US
dc.subjectSystematic literature review (SLR)en_US
dc.titleSoftware defect prediction using ensemble learning: A systematic literature reviewen_US
dc.typeReviewen_US

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