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On the applicability of search-based algorithms for software change prediction

Author

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  • Ruchika Malhotra

    (Delhi Technological University)

  • Megha Khanna

    (Delhi Technological University
    Sri Guru Gobind Singh College of Commerce, University of Delhi)

Abstract

Numerous research studies have claimed that search-based algorithms have the potential to be effectively used in various software engineering domains. An important task in software organizations is to efficiently recognize change prone classes of a software, as it is crucial to plan efficient resource utilization and to take precautionary design measures as early as possible in the software product lifecycle. This assures development of good quality software products at lower costs. The current study attempts to evaluate the capability of search-based algorithms while developing prediction models for identification of the change prone classes in a software. Though previous literature has evaluated the use of statistical category and machine learning category of algorithms in this domain, the suitability of search-based algorithms needs extensive investigation in this area. Furthermore, the study compares the performance of search-based classifiers with statistical and machine learning classifiers, by empirically validating the results on fourteen open source data sets. The results indicate comparable and in some cases even better performance of search based algorithms in comparison to other evaluated categories of algorithms.

Suggested Citation

  • Ruchika Malhotra & Megha Khanna, 2023. "On the applicability of search-based algorithms for software change prediction," 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 55-73, February.
    • Handle: RePEc:spr:ijsaem:v:14:y:2023:i:1:d:10.1007_s13198-021-01099-7
    DOI: 10.1007/s13198-021-01099-7
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    References listed on IDEAS

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    1. Sachin Kumar & Jagvinder Singh & Ompal Singh, 2020. "Ensemble-based extreme learning machine model for occupancy detection with ambient attributes," 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. 11(2), pages 173-183, July.
    2. Sabri Boughorbel & Fethi Jarray & Mohammed El-Anbari, 2017. "Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-17, June.
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