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Astract: Model integration is one of the core components for the realization of model-driven engineering. In particular, the seamless exchange of models among different modeling tools is of special importance. This exchange is achieved by the means of model transformations. However, the manual definition of model transformations is an error prone and cumbersome task. So matching techniques, originally intended for database schema integration, have been reused. The result is unsatisfactory as current matching approaches typically produce only one-toone alignments which are inappropriate for many integration problems. As a consequence, a detailed review and a manual post-processing step is often necessary. To tackle these problems, we propose the self-tuning framework SmartMatcher for improving automatically generated transformations. Our approach combines the power of an executable mapping language for bridging structural heterogeneities with the strength of an instance based quality evaluation model. In an iterative, feedback-driven process a mapping between two schemas is constructed and repeatedly enhanced.

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Astract: The vision of Model-Driven Engineering places models as first-class artifacts throughout the software lifecycle. An essential prerequisite is the availability of proper transformation languages allowing not only code generation but also augmentation, migration or translation of models themselves. Current approaches, however, lack convenient facilities for debugging and ensuring the understanding of the transformation process. To tackle these problems, we propose a novel formalism for the development of model transformations which is based on Colored Petri Nets. This allows first, for an explicit, process-oriented execution model of a transformation, thereby overcoming the impedance mismatch between the specification and execution of model transformations, being the prerequisite for convenient debugging. Second, by providing a homogenous representation of all artifacts involved in a transformation, including metamodels, models and the actual transformation logic itself, understandability of model transformations is enhanced.

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Astract: Realizing information exchange is a frequently recurring challenge in nearly every domain of computer science. Although languages, formalisms, and storage formats may differ in various engineering areas, the common task is bridging schema heterogeneities in order to transform their instances. Hence, a generic solution for realizing information exchange is needed. Conventional techniques often fail, because alignments found by matching tools cannot be executed automatically by transformation tools. In this paper we present the Smart Matching approach, a successful combination of matching techniques and transformation techniques, extended with self-tuning capabilities. With the Smart Matching approach, complete and correct executable mappings are found in a test-driven manner.