Software Defect Prediction Using Ensemble Learning: An ANP Based Evaluation Method

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dc.contributor.authorBalogun, Abdullateef Oluwagbemiga
dc.contributor.authorBajeh, Amos Orenyi
dc.contributor.authorOrie, Victor Agwu
dc.contributor.authorYusuf-Asaju, Wuraola Ayisat
dc.date.accessioned2018-12-20T09:45:05Z
dc.date.available2018-12-20T09:45:05Z
dc.date.issued2018-09
dc.description.abstractSoftware defect prediction (SDP) is the process of predicting defects in software modules, it identifies the modules that are defective and require extensive testing. Classification algorithms that help to predict software defects play a major role in software engineering process. Some studies have depicted that the use of ensembles is often more accurate than using single classifiers. However, variations exist from studies, which posited that the efficiency of learning algorithms might vary using different performance measures. This is because most studies on SDP consider the accuracy of the model or classifier above other performance metrics. This paper evaluated the performance of single classifiers (SMO, MLP, kNN and Decision Tree) and ensembles (Bagging, Boosting, Stacking and Voting) in SDP considering major performance metrics using Analytic Network Process (ANP) multi-criteria decision method. The experiment was based on 11 performance metrics over 11 software defect datasets. Boosted SMO, Voting and Stacking Ensemble methods ranked highest with a priority level of 0.0493, 0.0493 and 0.0445 respectively. Decision tree ranked highest in single classifiers with 0.0410. These clearly show that ensemble methods can give better classification results in SDP and Boosting method gave the best result. In essence, it is valid to say that before deciding which model or classifier is better for software defect prediction, all performance metrics should be considered.en_US
dc.identifier.citationBalogun, A. O., Bajeh, A. O., Orie, V. A., & Yusuf-Asaju, A.W. (2018): Software Defects Prediction using Ensemble Learning: An ANP Based Evaluation Method. FUOYE Journal of Engineering and Technology. 3(2); 50-55en_US
dc.identifier.issn2579-0625
dc.identifier.urihttp://hdl.handle.net/123456789/1524
dc.language.isoenen_US
dc.publisherFUOYE Journal of Engineering and Technology, Faculty of Engineering, Federal University Oye-Ekiti, Ekiti State, Nigeria.en_US
dc.relation.ispartofseries3;2
dc.subjectSoftware Engineeringen_US
dc.subjectData Miningen_US
dc.subjectKnowledge Discoveryen_US
dc.subjectSoftware Development Processen_US
dc.subjectSoftware Quality Assuranceen_US
dc.subjectMachine Learningen_US
dc.subjectMulti Criteria Decision Makingen_US
dc.subjectSoftware Defect Predictionen_US
dc.titleSoftware Defect Prediction Using Ensemble Learning: An ANP Based Evaluation Methoden_US
dc.typeArticleen_US

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