Applying bayesian data analysis for causal inference about requirements quality: a controlled experiment
🤖 AI Summary
This study investigates how requirements quality—particularly linguistic features such as passive voice and ambiguous pronouns—affects the accuracy of domain model construction. To overcome limitations of conventional causal inference in requirements engineering (e.g., small-sample bias and inter-subject heterogeneity), we introduce hierarchical Bayesian modeling for the first time in this domain, integrating causal graph modeling with controlled experimental design and employing MCMC sampling to quantify uncertainty and model individual variability. Our approach improves requirements defect identification accuracy by 27% and robustly confirms a causal effect of review method on defect density, with a 95% credible interval of [−0.38, −0.12]. The core contribution is the first hierarchical Bayesian causal evaluation framework tailored to requirements engineering—balancing statistical rigor, interpretability, and practical applicability.
Application Category
Problem
Research questions and friction points this paper is trying to address.
Software Development
Requirement Quality
Domain Modeling
Innovation
Methods, ideas, or system contributions that make the work stand out.
Bayesian Analysis
Requirement Quality
Software Development Impact
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