Measuring the Evolution of Meta-Models - A Case Study of Modelica and UML Meta-Models

Paper in proceeding, 2017

The evolution of both general purpose and domain-specific meta-models and its impact on the existing models and modeling tools has been discussed extensively in the modeling research community. To assess the impact of domain-specific meta-model evolution on the modeling tools, a number of measures have been proposed by Durisic et al., NoC (Number of Changes) being the most prominent one. The proposed measures are evaluated on a case of AUTOSAR meta-model that specifies the language for designing automotive system architectures. In this paper, we assess the applicability of these measure and the underlying data-model for their calculation in a case study of Modelica and UML meta-models. Our preliminary results show that the proposed data-model and the measures can be applied to both analyzed meta-models as we were able to capture 68/77 changes on average per Modelica/UML release. However, only a subset of the data-model elements is applicable for analyzing the evolution of Modelica and also certain transformation of the data-model is required in case of UML. Despite these encouraging results, further studies are needed to assess the usefulness of the actual measures, e.g., NoC, in assessing the impact of Modelica/UML meta-model evolution on the modeling tools.