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An effective feature selection based cross-project defect prediction model for software quality improvement

Author

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  • Yogita Khatri

    (Jaypee Institute of Information Technology)

  • Sandeep Kumar Singh

    (Jaypee Institute of Information Technology)

Abstract

Cross-project defect prediction (CPDP) involves the use of other projects (aka source projects) for training and persuasive model building for a particular project (aka target project). However, the distribution dissimilarity between the two different project data often limits the CPDP model’s capability. Several CPDP approaches have been proposed in the literature to combat this distribution gap through instance selection by transferring the knowledge learned from the source to the target project. However, very few have explored transferring knowledge through feature selection (FS). A novel CPDP approach has been proposed consisting of two distinct FS strategies (one non-iterative and one iterative) having a trade-off between the cost and the performance respectively. The first strategy MIC_SM_FS is a non-iterative strategy that selects features that are important and have similar distribution with the corresponding target feature. The feature importance is measured using maximal information coefficient and the feature distribution similarity is calculated using 10 statistical measures. On the other hand, the second strategy BPSO_FS is an iterative strategy that works on optimizing the performance, utilizing the powerful binary particle swarm optimization algorithm for selecting the representative features for CPDP. Both of the proposed strategies have been tested on 26 cross-project experiments based on 8 software projects. From the two proposed strategies, a CPDP model built utilizing BPSO_FS showed better results. Further, to assess its performance, comparison is done with two baseline approaches viz. ALL and ManualDown, within-project defect prediction, and a state-of-the-art CPDP technique TCA+. Statistical results showed the potential of the proposed CPDP approach over the compared approaches.

Suggested Citation

  • Yogita Khatri & Sandeep Kumar Singh, 2023. "An effective feature selection based cross-project defect prediction model for software quality improvement," 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. 14(1), pages 154-172, March.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:1:d:10.1007_s13198-022-01831-x
    DOI: 10.1007/s13198-022-01831-x
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    References listed on IDEAS

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    1. Arunima Jaiswal & Ruchika Malhotra, 2018. "Software reliability prediction using machine learning techniques," 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. 9(1), pages 230-244, February.
    2. Taghi M. Khoshgoftaar & Kehan Gao & Amri Napolitano & Randall Wald, 2014. "A comparative study of iterative and non-iterative feature selection techniques for software defect prediction," Information Systems Frontiers, Springer, vol. 16(5), pages 801-822, November.
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