Multilevel Modelling (MLM) approaches make it possible for designers and modellers to work with an unlimited number of abstraction levels to specify their domain-specific modelling languages (DSMLs). To fully exploit MLM techniques, we need powerful model composition operators. Indeed, the composition of DSMLs is becoming increasingly relevant to the modelling community either because some DSMLs may share commonalities that we want to make reusable, or because we want to facilitate interoperability between DSMLs. In this paper, we propose a composition mechanism for structure and behaviour of multilevel modelling hierarchies. Our approach facilitates the inclusion of additional features while keeping a clear separation of concerns that enhances modularity. We provide a formal semantics of the constructions based on category theory and graph transformations, and show their use in practice on a case study.

Model Driven Engineering (MDE) is essentially based in metamodel definition, model edition and the specification of model transformations (MT) among these. In many cases the development, evolution and adaptation of these transformations is still carried out without the support of proper methods and tools to reduce the effort and related costs to these activities. In this work, a novel model testing approach specifically designed to assist the engineer in model transformation evolution is presented. A statistical analysis of the actual behavior of the transformations is performed by means of the computation of well-known information extraction metrics. In order to assist the MT adaptation, a detailed interpretation of the possible results of those metrics is also presented. And finally, the results of applying this approach on a Model-Driven Reverse Engineering (MDRE) scenario defined in the context of the MIGRARIA project are discussed.