FBS26: An Entropy-Based Metric for Assessing Architectural Maturity of UML Class Diagrams

Entropy-based metrics can provide a quantitative representation of the information content of UML models, but their practical usefulness depends on a systematic and reproducible calibration of the parameters used to construct the underlying probability distribution. This paper presents FBS26, an extension of the FBS metric that introduces calibrated weights for UML element kinds while retaining the normalized Shannon entropy formulation. The proposed calibration procedure combines three criteria: avoiding false lack-of-information cases, reducing non-monotonic behavior of the metric, and achieving a pronounced decrease in the metric for balanced diagram series. The criteria are applied according to a predefined lexicographic priority. The approach is evaluated using a controlled series of UML class diagrams and 123 UML class diagrams from four industrial software projects, selected from a repository containing more than 2000 UML diagrams from 18 projects. Architectural maturity labels, assigned independently by an experienced software architect, serve as reference data for examining empirical thresholds for structural information. The results show that systematic weight calibration removes the undesirable behavior observed for the considered FBS16 configurations and provides a more stable basis for comparing UML class diagrams. The industrial analysis further indicates differences in the structural entropy ranges of commonly distinguished diagram categories. FBS26, therefore, provides a quantitative information-content measure that can support expert-based assessment of the architectural maturity of UML class diagrams.