GMF: A Model Migration Case for the Transformation Tool Contest

Using a real-life evolution taken from the Graphical Modeling Framework, we invite submissions to explore ways in which model transformation and migration tools can be used to migrate models in response to metamodel adaptation.Comment: In Proceedings TTC 2011, arXiv:1111.440

A Comparison of Model Migration Tools

International audienceModelling languages and thus their metamodels are subject to change. When a metamodel evolves, existing models may no longer conform to the evolved metamodel. To avoid rebuilding them from scratch, existing models must be migrated to conform to the evolved metamodel. Manually migrating existing models is tedious and errorprone. To alleviate this, several tools have been proposed to build a migration strategy that automates the migration of existing models. Little is known about the advantages and disadvantages of the tools in different situations. In this paper, we thus compare a representative sample of migration tools - AML, COPE, Ecore2Ecore and Epsilon Flock - using common migration examples. The criteria used in the comparison aim to support users in selecting the most appropriate tool for their situation

Solving the TTC 2011 Model Migration Case with Edapt

This paper gives an overview of the Edapt solution to the GMF model migration case of the Transformation Tool Contest 2011.Comment: In Proceedings TTC 2011, arXiv:1111.440

A Tool for Supporting the Co-Evolution of Enterprise Architecture Meta-models and Models

Enterprise architecture models capture the concepts and relationships that together describe the essentials of the various enterprise domains. This model of the enterprise is tightly coupled to a domain-specific modeling language that defines the formalisms for creating and updating such model. These languages are described as meta-models by the model-driven engineering field. Results from surveys on enterprise architecture tool analysis showed a lack of support concerning the co-evolution of enterprise architecture meta-model and models. This paper presents a tool that automates enterprise architecture models co-evolution according to a set of meta-model changes. A Portuguese governmental organization used and validated the tool using observational, analytical and descriptive evaluation methods

Evolving models in Model-Driven Engineering : State-of-the-art and future challenges

The artefacts used in Model-Driven Engineering (MDE) evolve as a matter of course: models are modified and updated as part of the engineering process; metamodels change as a result of domain analysis and standardisation efforts; and the operations applied to models change as engineering requirements change. MDE artefacts are inter-related, and simultaneously constrain each other, making evolution a challenge to manage. We discuss some of the key problems of evolution in MDE, summarise the key state-of-the-art, and look forward to new challenges in research in this area

Synthesis of Model Transformations from Metamodels and Examples

Model transformations are central elements of model-driven engineering (MDE). However, model transformation development requires a high level of expertise in particular model transformation languages, and model transformation specifications are often difficult to manually construct, due to the lack of tool support, and the dependencies involved in transformation rules.In this thesis, we describe techniques for automatically or semi-automatically synthesising transformations from metamodels and examples, in order to reduce model transformation development costs and time, and improve model transformation quality.We proposed two approaches for synthesising transformations from metamodels. The first approach is the Data Structure Similarity Approach, an exhaustive metamodel matching approach, which extracts correspondences between metamodels by only focusing on the type of features. The other approach is the Search-based Optimisation Approach, which uses an optimisation algorithm to extract correspondences from metamodels by data structure similarity, name syntax similarity, and name semantic similarity. The correspondence patterns between the classes and features of two metamodels are extracted by either of these two methods. To enable the production of specifications in multiple model transformation languages from correspondences, we introduced an intermediate language which uses a simplified transformation notation to express transformation specifications in a language-independent manner, and defined the mapping rules from this intermediate language to different transformation languages.We also investigated Model Transformation by Examples Approach. We used machine learning techniques to learn model transformation rules from datasets of examples, so that the trained model could generate target model from source model directly.We evaluated our approaches on a range of cases of different kinds of transformation, and compared the model transformation accuracy and quality of our versions to the previously-developed manual versions of these cases.Key words: model transformation, model-driven engineering, transformation syn-thesis, metamodel matching, model transformation by example