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

Chandan Kumar (), Dilip Kumar Yadav and Mukesh Prasad
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Chandan Kumar: Amrita School of Computing, Amrita Vishwa Vidyapeetham
Dilip Kumar Yadav: NIT Jamshedpur
Mukesh Prasad: University of Technology

International Journal of System Assurance Engineering and Management, 2025, vol. 16, issue 6, No 14, 2204-2218

Abstract: 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.

Keywords: Fault-Prone software module; Bayesian belief network; Fuzzy reasoning; Software metrics; Software quality (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13198-025-02809-1

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