In building software architectures, the relations between elements in different diagrams are often overlooked. The first stage of building IT systems is the use of ontology terminology, not software terminology, in the requirements engineering process. Then, when constructing software architecture, IT architects more or less consciously however introduce elements that represent the same classifier on different diagrams with similar names. These connections are called consistency rules and are usually not attached in any way in a modeling tool, and only a significant number of them in the models increase the quality of the software architecture. It is mathematically proved that the application of consistency rules increases the information content of software architecture. Authors show that increasing readability and ordering of software architecture by means of consistency rules have their mathematical rationale. In this article, we found proof of decreasing Shannon entropy while applying consistency rules in the construction of software architecture of IT systems. Therefore, it has been shown that marking selected elements in different diagrams with these same names is, therefore, an implicit way to increase the information content of software architecture while simultaneously improving its orderliness and readability. Moreover, this increase in the quality of the software architecture can be measured by entropy, which allows for checking whether the number of consistency rules is sufficient to compare different architectures, even of different sizes, thanks to entropy normalization, and checking during the development of the software architecture, what is the improvement in its orderliness and readability.

Various metrics exist for evaluating UML diagrams, including entropy-based ones like ours, which assess information content. They allow for judging certain features of the design that depend on the information content. This paper proposes the FBS24 use case diagram measure, which should ensure that the software architecture design consists of mature UML diagrams. Detecting an inappropriate (immature, unfinished) Use Case Diagram before the software development phase can stall the entire software development process until a mature UCD is developed. Currently, no indicators (metrics) show the maturity (or lack of applicability) of diagrams. Moreover, in most such software metrics, weights are selected arbitrarily, which leads to numerous anomalies. We show how to construct the measure most resistant to such anomalies. We also show how to check the correctness and usefulness of the constructed measure. As our measure is objective, it is suitable for remote work of distributed teams building IT systems.

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.