Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers

Akinbowale Nathaniel Babatunde, Roseline Oluwaseun Ogundokun, Latifat Bukola Adeoye and Sanjay Misra ()
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Akinbowale Nathaniel Babatunde: Department of Computer Science, Kwara State University, Ilorin 241103, Nigeria
Roseline Oluwaseun Ogundokun: Department of Multimedia Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania
Latifat Bukola Adeoye: Department of Computer Science, University of Ilorin, Ilorin 240003, Nigeria
Sanjay Misra: Department of Applied Data Science, Institute of Energy Technology, 1777 Halden, Norway

Mathematics, 2023, vol. 11, issue 12, 1-18

Abstract: To guarantee that software does not fail, software quality assurance (SQA) teams play a critical part in the software development procedure. As a result, prioritizing SQA activities is a crucial stage in SQA. Software defect prediction (SDP) is a procedure for recognizing high-risk software components and determining the influence of software measurements on the likelihood of software modules failure. There is a continuous need for sophisticated and better SDP models. Therefore, this study proposed the use of dagging-based and baseline classifiers to predict software defects. The efficacy of the dagging-based SDP model for forecasting software defects was examined in this study. The models employed were naïve Bayes (NB), decision tree (DT), and k-nearest neighbor (kNN), and these models were used on nine NASA datasets. Findings from the experimental results indicated the superiority of SDP models based on dagging meta-learner. Dagging-based models significantly outperformed experimented baseline classifiers built on accuracy, the area under the curve (AUC), F-measure, and precision-recall curve (PRC) values. Specifically, dagging-based NB, DT, and kNN models had +6.62%, +3.26%, and +4.14% increments in average accuracy value over baseline NB, DT, and kNN models. Therefore, it can be concluded that the dagging meta-learner can advance the recognition performances of SDP methods and should be considered for SDP processes.

Keywords: classification algorithm; defect prediction; software quality assurance; dagging meta-learner (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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