Code Generation from UML Models with Semantic Variation Points

Abstract

AbstractUML semantic variation points provide intentional degrees of freedom for the interpretation of the metamodel semantics. The interest of semantic variation points is that UML now becomes a family of languages sharing lot of commonalities and some variabilities that one can customize for a given application domain. In this paper, we propose to reify the various semantic variation points of UML 2.0 statecharts into models of their own to avoid hardcoding the semantic choices in the tools. We do the same for various implementation choices. Then, along the line of the OMG’s Model Driven Architecture, these semantic and implementation models are processed along with a source UML model (that can be seen as a PIM) to provide a target UML model (a PSM) where all semantic and implementation choice are made explicit. This target model can in turn serve as a basis for a consistent use of code generation, simulation, model-checking or test generation tools.KeywordsCode GenerationModel TransformationSemantic ModelSelection PolicyStructure Operational SemanticThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.