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The theory of model transformation has been studied extensively during the last decade and is now well understood. Several transformation languages have been developed and implemented. Recently, model matching has been proposed to offer support for transformation development. The task of model matching aims at finding semantic correspondences between model elements, thus facilitating semi-automatic mapping generation. However, current model matching approaches mostly concentrate on label-based model similarity and are isolated. Further, they show deficits with respect to quality, performance and language independence. We tackle these issues by proposing a novel approach using a combination of matchers in a common framework. Thereby, schema matching techniques are adapted and extended to suit our needs. Complementing our approach, we propose model specific matchers addressing new aspects of similarity. Our configurable framework allows an interpretation of combined matching results, thus increasing the number and quality of mappings found.

Towards combining Model Matchers for Transformation Development