On Combining Multi-formalism Knowledge to Select Models for Model Transformation Testing

Testing remains a major challenge for model transformation development. Test models that are used as test data for model transformations, are constrained by various sources of knowledge that is expressed in different formalisms. Thus, in order to automatically generate test models it is necessary to interpret these different sources of knowledge and combine them into a consistent set of information that can be used for model synthesis. In this paper, we identify sources of testing knowledge and present our tool Cartier that uses Alloy as the first-order relational logic language to represent combined knowledge in the form of constraints. The constraints are solved leading to a selection of qualified test models from the input domain of a model transformation. We illustrate our approach using the Unified Modeling Language class diagram to relational database management systems transformation as a running example.