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Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers

Author

Listed:
  • 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
    Department of Computer Science, Landmark University, Omu Aran 251103, Nigeria)

  • 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)

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.

Suggested Citation

  • Akinbowale Nathaniel Babatunde & Roseline Oluwaseun Ogundokun & Latifat Bukola Adeoye & Sanjay Misra, 2023. "Software Defect Prediction Using Dagging Meta-Learner-Based Classifiers," Mathematics, MDPI, vol. 11(12), pages 1-18, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:12:p:2714-:d:1171880
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    References listed on IDEAS

    as
    1. Xuan Liu & Zehao Li & Xinyi Fu & Zhengtong Yin & Mingzhe Liu & Lirong Yin & Wenfeng Zheng, 2023. "Monitoring House Vacancy Dynamics in The Pearl River Delta Region: A Method Based on NPP-VIIRS Night-Time Light Remote Sensing Images," Land, MDPI, vol. 12(4), pages 1-21, April.
    2. Ruba Abu Khurma & Hamad Alsawalqah & Ibrahim Aljarah & Mohamed Abd Elaziz & Robertas Damaševičius, 2021. "An Enhanced Evolutionary Software Defect Prediction Method Using Island Moth Flame Optimization," Mathematics, MDPI, vol. 9(15), pages 1-20, July.
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