Emergent Mind

Systematically Deriving Domain-Specific Transformation Languages

(1511.05366)
Published Nov 17, 2015 in cs.SE

Abstract

Model transformations are helpful to evolve, refactor, refine and maintain models. While domain-specific languages are normally intuitive for modelers, common model transformation approaches (regardless of whether they transform graphical or textual models) are based on the modeling language's abstract syntax requiring the modeler to learn the internal representation of the model to describe transformations. This paper presents a process that allows to systematically derive a textual domainspecific transformation language from the grammar of a given textual modeling language. As example, we apply this systematic derivation to UML class diagrams to obtain a domain-specific transformation language called CDTrans. CDTrans incorporates the concrete syntax of class diagrams which is already familiar to the modeler and extends it with a few transformation operators. To demonstrate the usefulness of the derived transformation language, we describe several refactoring transformations.

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