NDLI: Software Defect Prediction Using Ensemble Learning: A Systematic Literature Review

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Faseeha Matloob Taher M. Ghazal Nasser Taleb Shabib Aftab Munir Ahmad Muhammad Adnan Khan Sagheer Abbas Tariq Rahim Soomro
Abstract	Recent 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.
e-ISSN	21693536
DOI	10.1109/ACCESS.2021.3095559
Journal	IEEE Access
Volume Number	9
Language	English
Publisher	IEEE
Publisher Date	2021-01-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Electrical Engineering. Electronics. Nuclear Engineering Systematic Literature Review (slr) Ensemble Classifier Hybrid Classifier Software Defect Prediction
Content Type	Text
Resource Type	Article

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