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Predicting fault-prone software modules using bayesian belief network: an empirical study

Author

Listed:
  • Chandan Kumar

    (Amrita School of Computing, Amrita Vishwa Vidyapeetham)

  • Dilip Kumar Yadav

    (NIT Jamshedpur)

  • Mukesh Prasad

    (University of Technology)

Abstract

Predicting software modules prone to faults has become a prominent study focus within software engineering, aiming to spot probable defects early and optimize the allocation of quality assurance efforts. This study proposes a methodology for prediction of fault-prone software modules using a Bayesian Belief Network (BBN). The approach begins by applying information gain-based attribute ranking to a numerical dataset—specifically, the KC1 class-level dataset from the NASA project—categorizing the most effective software metrics. The BBN model is proposed using the top-ranked “Chidamber and Kemerer (CK)” metric suite and a conventional code-size metric. The proposed model is experimented with KC1 data set and validate with previous work. The Comparative evaluation proves that the proposed model achieves better accuracy at 77.93% in fault-prone modules prediction as compared to the previous models that had 67.57% and 75.17%, respectively. The strength of this methodology is based on its systematic integration of information gain attribute ranking, fuzzy reasoning process, and BBN approach, highlighting its effectiveness in advancing fault-prone module prediction.

Suggested Citation

  • Chandan Kumar & Dilip Kumar Yadav & Mukesh Prasad, 2025. "Predicting fault-prone software modules using bayesian belief network: an empirical study," 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. 16(6), pages 2204-2218, June.
    • Handle: RePEc:spr:ijsaem:v:16:y:2025:i:6:d:10.1007_s13198-025-02809-1
    DOI: 10.1007/s13198-025-02809-1
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