In the Model-Driven Architecture (MDA) paradigm the Query/View/Transformation (QVT) standard plays a vital role for model transformations. Especially the high-level declarative QVT Relations language, however, has not yet gained widespread use in practice. This is not least due to missing tool support in general and inadequate debugging support in particular. Transformation engines interpreting QVT Relations operate on a low level of abstraction, hide the operational semantics of a transformation and scatter metamodels, models, QVT code, and trace information across different artifacts.We therefore propose a model-based debugger representing QVT Relations on bases of TROPIC, a model transformation language utilizing a variant of Colored Petri Nets (CPNs). As a prerequisite for convenient debugging, TROPIC provides a homogeneous view on all artifacts of a transformation on basis of a single formalism. Besides that, this formalism also provides a runtime model, thus making the afore hidden operational semantics of the transformation explicit. Using an explicit runtime model allows to employ model-based techniques for debugging, e.g., using the Object Constraint Language (OCL) for simply defining breakpoints and querying the execution state of a transformation.
Abstract. The standardized QVT Relations language, one cornerstone of Model-Driven Architecture (MDA), has not yet gained widespread use in practice, not least due to missing tool support in general and inadequate debugging support in particular. Transformation engines interpreting QVT Relations operate on a low level of abstraction, hide the operational semantics of a transformation and scatter metamodels, models, QVT code, and traces across different artifacts. We propose a model-based debugger representing QVT Relations on bases of TROPIC, a model transformation framework which utilizes a variant of Colored Petri Nets (CPNs) providing an explicit runtime model and a homogenous view on all artifacts of a transformation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.