Towards the efficient development of model transformations using model weaving and matching transformations

Model transformations can be used in many different application scenarios, for instance, to provide interoperability between models of different size and complexity. As a consequence, they are becoming more and more complex. However, model transformations are typically developed manually. Several code patterns are implemented repetitively, thus increasing the probability of programming errors and reducing code reusability. There is not yet a complete solution that automates the development of model transformations. In this paper, we present a novel approach that uses matching transformations and weaving models to semi-automate the development of transformations. Weaving models are models that contain different kinds of relationships between model elements. These relationships capture different transformation patterns. Matching transformations are a special kind of transformations that implement methods that create weaving models. We present a practical solution that enables the creation and the customization of different creation methods in an efficient way. We combine different methods, and present a metamodel-based method that exploits metamodel data to automatically produce weaving models. The weaving models are derived into model integration transformations. To validate our approach, we present an experiment using metamodels with distinct size and complexity, which show the feasibility and scalability of our solution.