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Software fault proneness prediction: a comparative study between bagging, boosting, and stacking ensemble and base learner methods

Author

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  • Mohammed Akour
  • Izzat Alsmadi
  • Iyad Alazzam

Abstract

Modules with defects might be the prime reason for decreasing the software quality and increasing the cost of maintenance. Therefore, the prediction of faulty modules of systems under test at early stages contributes to the overall quality of software products. In this research three symmetric ensemble methods: bagging, boosting and stacking are used to predict faulty modules based on evaluating the performance of 11 base learners. The results reveal that the defect prediction performance of the base learner classifier and ensemble learner classifiers is the same for naïve Bayes, Bayes net, PART, random forest, IB1, VFI, decision table, and NB tree base learners, the case was different for boosted SMO, bagged J48 and boosted and bagged random tree. In addition the results showed that the random forest classifier is one of the most significant classifiers that should be stacked with other classifiers to gain the better fault prediction.

Suggested Citation

  • Mohammed Akour & Izzat Alsmadi & Iyad Alazzam, 2017. "Software fault proneness prediction: a comparative study between bagging, boosting, and stacking ensemble and base learner methods," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 9(1), pages 1-16.
    • Handle: RePEc:ids:injdan:v:9:y:2017:i:1:p:1-16
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    Citations

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    Cited by:

    1. Sara Saadatmand & Khodakaram Salimifard & Reza Mohammadi & Alex Kuiper & Maryam Marzban & Akram Farhadi, 2023. "Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients," Annals of Operations Research, Springer, vol. 328(1), pages 1043-1071, September.
    2. Yiheng Li & Weidong Chen, 2020. "A Comparative Performance Assessment of Ensemble Learning for Credit Scoring," Mathematics, MDPI, vol. 8(10), pages 1-19, October.
    3. Inderpreet Kaur & Arvinder Kaur, 2021. "Comparative analysis of software fault prediction using various categories of classifiers," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(3), pages 520-535, June.

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