Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers
Loading...
Date
2023-06-15
Journal Title
Journal ISSN
Volume Title
Publisher
MPDI
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