Model Transformation using Constraint Handling Rules as a basis for Model Interpretation

Marcel Dausend and Frank Raiser. Model Transformation using Constraint Handling Rules as a basis for Model Interpretation. 2011, Workshop

Abstract:

In this paper, we present a model transformation approach aiming to simplify automatic processing of UML state machine models, especially for interpretation. The main requirements are easing the implementation of the interpreter and reducing the number of calculations necessary to execute a model. Our model transformation preserves the semantics and is implemented using CHR. The result of the transformation is an UML state machine model based on the concept of compound transitions. Furthermore we provide an interpreter for those models which supports a comprehensive subset of UML state machine concepts, i. a. junction, fork, join. Our preliminary results show that state machine interpreters can profit from the former model transformation. It simplifies certain aspects of the interpreter implementation and positively affects the performance of the interpreter, e.g. regarding transition selection and transition execution.