Section: New Results

Model Transformation Testing

White-box testing for model transformations is a technique that involves the extraction of knowledge embedded in the transformation code to generate test models. In [31] , we manually extract such knowledge and we represent it in the form of partial models that can drive the generation of highly effective test models. In other works we go a step further and use static analysis to automatically extract testing knowledge from transformation code. We propose two tool-supported methodologies to automatically generate test cases using structural information from a model transformation. In [27] we have developed an approach that optimizes the test coverage while testing rule-based model transformation languages like ATL. The approach is based on analyzing the dependencies among the OCL queries that are used within the transformation code. The methodology in [29] makes use of the metamodel footprinting mechanism, generates partial models representing the testing intent and uses the ALLOY solver to create complete usable models. The experimental results show that a limited amount of white-box information on the model transformation (i.e., our footprints) can provide remarkable improvements on the efficiency of the generated tests.