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<p>Accurate estimation of software defects is essential in building reliable and cost-efficient systems, especially when predictions must be made at an early stage of the Software Development Life Cycle (SDLC). This paper presents a human-in-the-loop causal estimation model that incorporates Bayesian Belief Networks (BBNs) and fuzzy logic to represent uncertainty and establish relationships among top-ranked metrics from the requirements, design, and coding phases. In contrast to other hybrid BBN–fuzzy approaches, the proposed architecture directly combines expert judgment, novice assessments, and probabilistic inference, facilitating effective defect estimation in environments where skilled experts are in short supply. The model was evaluated using a sample of 31 software projects from prior work to assess early-phase causal defect estimation under data-sparse conditions. Statistical evaluation using Mean Magnitude of Relative Error (MMRE), Balanced Mean Magnitude of Relative Error (BMMRE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and nonparametric significance testing demonstrates that the proposed approach achieves improved estimation accuracy relative to benchmark methods. The Wilcoxon signed-rank test indicates a statistically significant reduction in estimation error for the proposed framework compared to the benchmark methods. Notably, novice estimators, when guided by the causal model, generated estimates closely aligned with expert assessments, highlighting the usefulness of the framework in agile and resource-constrained development settings.</p>

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Causal modeling in software defect prediction: bridging expert knowledge and novice insight

  • 期刊论文

  • 发表日期：

    2026年5月13日

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  • Chandan Kumar,
  • Umashankar Samal,
  • Tony Jan,
  • Kamran Shaukat,
  • Mukesh Prasad

摘要

Accurate estimation of software defects is essential in building reliable and cost-efficient systems, especially when predictions must be made at an early stage of the Software Development Life Cycle (SDLC). This paper presents a human-in-the-loop causal estimation model that incorporates Bayesian Belief Networks (BBNs) and fuzzy logic to represent uncertainty and establish relationships among top-ranked metrics from the requirements, design, and coding phases. In contrast to other hybrid BBN–fuzzy approaches, the proposed architecture directly combines expert judgment, novice assessments, and probabilistic inference, facilitating effective defect estimation in environments where skilled experts are in short supply. The model was evaluated using a sample of 31 software projects from prior work to assess early-phase causal defect estimation under data-sparse conditions. Statistical evaluation using Mean Magnitude of Relative Error (MMRE), Balanced Mean Magnitude of Relative Error (BMMRE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and nonparametric significance testing demonstrates that the proposed approach achieves improved estimation accuracy relative to benchmark methods. The Wilcoxon signed-rank test indicates a statistically significant reduction in estimation error for the proposed framework compared to the benchmark methods. Notably, novice estimators, when guided by the causal model, generated estimates closely aligned with expert assessments, highlighting the usefulness of the framework in agile and resource-constrained development settings.