Modeling Language Evolution for Model Family Support

In Model-Driven Engineering, models can evolve over time or vary along dimensions such as products. Such evolution results in a set of related models called model family. A model family can be captured with a “150% model” that merges the family members, while enabling the extraction of the individual models. In this context however, a 150% model may no longer conform to the original metamodel of the family members. This paper presents my Ph.D. research agenda on inferring the metamodel of a model family from the structure of the metamodel of its members. In particular, I aim to define a technique that minimally relaxes the original metamodel constraints related to multiplicities of attributes and association ends. Although a simpler problem is to infer minimal constraint relaxations from the current family members, the more interesting problem is to predict where such relaxations are needed in the metamodel, so that existing tools and analysis techniques can be adapted once and minimally for a given modeling language. This work is applicable to the regulatory domain, for example, as regulations evolve and have variations that need to be captured and analyzed using slightly different goal models. Such work would also indirectly help the community gain a better understanding of the nature of metamodels. Keywords— Conformance; constraint relaxation; evolution; metamodel; Model-Driven Engineering; model family; variability; 150% model.