In Model-Driven Engineering, analogously to any software artifact, metamodels are equally prone to evolution. When a metamodel undergoes modifications, all the related artifacts must be accordingly adapted in order to remain valid. Manual co-evolution of models after these metamodel changes is error-prone. In this setting, this paper introduces a semiautomatic process for the co-evolution of models after metamodel evolution. The process is divided in four main stages: at the differencing stage, the changes to the metamodel are detected. After that these changes are linked with the original model elements and represented in a weaving model which serves to generate a transformation used in the last stage in order to obtain the evolved model. Contributions of this paper include the automatic co-evolution of breaking and resolvable changes and the assistance to the model developer in the co-evolution of breaking and un-resolvable changes.
.Evolution is an inevitable aspect which affects metamodels. When metamodels evolve, model conformity may be broken. Model co-evolution is critical in model driven engineering to automatically adapt models to the newer versions of their metamodels. In this paper we discuss what can be done to transfer models between versions of a metamodel. For this purpose we introduce hybrid approach for model and metamodel co-evolution, that first uses matching between two metamodels to discover changes and then applied evolution operators to migrate models. In this proposal, migration of models is done automatically; except, for non resolvable changes, where assistance is proposed to the users in order to co-evolve their models to regain conformity.
Evolution is inherent in software systems because of the rapid improvement of technologies. As metamodels are the cornerstone of model driven engineering and they evolve iteratively, their evolution affects the rest of artefacts involved in a development process, e.g., models or transformation rules. Therefore, co-evolution tools for models and other artefacts are indispensable. We present in this paper an intelligent approach to adapt models by means of state-based and operator-based techniques. The defined co-evolution process consists of four phases. Initially, changes between two metamodel versions are detected. After that, evolution scenario is reconstructed using logic programming method. Evolution scenario is first calculated from difference model and represented by a set of primitive evolution operations, after that it is transformed using an inference engine to include eventual composite evolution operations; from this scenario adaptation solutions are generated according to the evolution operator impact in model level. Finally, migration scenario is applied on an input model conforming to the old metamodel version to remain compliant with its metamodel.
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