As model-driven engineering (MDE) gets applied for the development of larger systems, the quality assurance of model-driven artifacts gets more important. Here, metamodels are particularly important as many other artifacts depend on them. Existing approaches to measure the modularity of metamodels have not been validated for metamodels thoroughly. In this paper, we evaluate the usage of the metrics suggested by Sarkar et al. to automatically measure the modularity of metamodels with the goal of automated quality improvements. For this, we analyze the data from a previous controlled experiment on the perception of metamodel quality with 24 participants, including both students and academic professionals. From the results, we were able to statistically disprove even a slight correlation with perceived metamodel quality.
Our paper on the usage of Sarkar metrics to evaluate the modularity of metamodels presents an alternative approach to obtain a quantitative quality assessment of metamodels REF .
On using sarkar metrics to evaluate the modularity of metamodels
MODELSWARD
Computer Science