Predicting the Perceived Modularity of MOF-based Metamodels

Resource type
Hinkel, Georg and Strittmatter, Misha
This research has received funding from the European Union Horizon 2020 Future and Emerging Technologies Programme (H2020-EU.1.2.FET) under grant agreement no. 720270 (Human Brain Project SGA-I) and the Helmholtz Association
Book title
Proceedings of the 6th International Conference on Model-Driven Engineering and Software Development
As model-driven engineering (MDE) gets applied for the development of larger systems, the quality assurance of model-driven artifacts becomes more important. Here, metamodels are particularly important as many other artifacts depend on them. However, existing metrics have been rarely validated for metamodels or, even more, evaluation results disproved a correlation between these existing metrics and perceived metamodel modularity. In this paper, we present a new entropy-based metric to capture the perception of metamodel modularity and evaluate the metric in multiple case studies. In the case studies, we correlate the metric results of 32 metamodels across three different domains with 164 responses of a quality assessment questionnaire for which we collected responses in two empirical experiments. The results show significant and strong correlations in all three domains between the metric results and the perceived metamodel modularity.
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