Schema Understandability: A Comprehensive Empirical Study of Requirements Metrics

Ensuring high-quality data warehouses is crucial for organizations, as they provide the reliable information needed for informed decision-making. While various methodologies emphasize the importance of requirements, conceptual, logical, and physical models in developing data warehouses, empirical quality assessment of these models remains underexplored, especially requirements models. To bridge this gap, this study focuses on assessment of requirements metrics for predicting the understandability of requirements schemas, a key indicator of model quality. In this empirical study, 28 requirements schemas were classified into understandable and non-understandable clusters using the k-means clustering technique. The study then employed six classification techniques—logistic regression, naive Bayes, linear discriminant analysis with decision tree, reinforcement learning, voting rule, and a hybrid approach—within both univariate and multivariate models to identify strong predictors of schema understandability. Results indicate that 13 out of 17 requirements metrics are robust predictors of schema understandability. Furthermore, a comparative performance analysis of the classification techniques reveals that the hybrid classifier outperforms other techniques across key evaluation parameters, including accuracy, sensitivity, specificity, and AUC. These findings highlight the potential of requirements metrics as effective predictors of schema understandability, contributing to improved quality assessment and the development of better conceptual data models for data warehouses.