Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers
| dc.contributor.author | Akinbowale Nathaniel Babatunde , Roseline Oluwaseun Ogundokun , Latifat Bukola Adeoye and Sanjay Misra | |
| dc.date.accessioned | 2023-07-15T13:05:40Z | |
| dc.date.available | 2023-07-15T13:05:40Z | |
| dc.date.issued | 2023-06-15 | |
| dc.description.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 | |
| dc.identifier.uri | https://kwasuspace.kwasu.edu.ng/handle/123456789/319 | |
| dc.publisher | MPDI | |
| dc.title | Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers |