Computer Science – Artificial Intelligence
Scientific paper
2010-03-03
ACM/IEEE 12th International Conference on Model Driven Engineering Languages and Systems, Denver : United States (2009)
Computer Science
Artificial Intelligence
Scientific paper
10.1007/978-3-642-04425-0_8
Model transformations operate on models conforming to precisely defined metamodels. Consequently, it often seems relatively easy to chain them: the output of a transformation may be given as input to a second one if metamodels match. However, this simple rule has some obvious limitations. For instance, a transformation may only use a subset of a metamodel. Therefore, chaining transformations appropriately requires more information. We present here an approach that automatically discovers more detailed information about actual chaining constraints by statically analyzing transformations. The objective is to provide developers who decide to chain transformations with more data on which to base their choices. This approach has been successfully applied to the case of a library of endogenous transformations. They all have the same source and target metamodel but have some hidden chaining constraints. In such a case, the simple metamodel matching rule given above does not provide any useful information.
Chenouard Raphael
Jouault Frédéric
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