Abstract. There is a growing need to explicitly represent the behavioral semantics of Modeling Languages in a precise way, something especially important in industrial environments in which simulation and verification are critical issues. Graph transformation provides one way to specify the semantics of Domain Specific Visual Languages (DSVLs), with the advantage of being intuitive and easy to use for the system designer. Even though its theory has been extensively developed during the last 30 years, it has some limitations concerning specific analysis capabilities. On the contrary, Maude is a rewriting logic-based language with very good formal analysis support, but which requires specialized knowledge. In this paper we show how a mapping between graph transformation-based specifications of DSVL semantics and Maude is possible. This allows performing simulation, reachability and model-checking analysis on the models, using the tools and techniques that Maude provides. 1

Abstract. Domain specific languages (DSLs) play a cornerstone role in Model-Driven Software Development for representing models and metamodels. DSLs are usually defined only in terms of their abstract and concrete syntaxes, although this hampers the development of formal analysis and simulation tools. In this paper we advocate the use of in-place model transformations to complement metamodels (the structural aspects of a DSL) with timed behavioral specifications. In particular, we propose an extension for in-place transformation rules to state action properties (not only model element properties), and to model time-dependent behavior. This approach avoids making unnatural changes to the DSL metamodels to represent behavioral and time aspects, and allows the resulting specifications to be translated into different semantic domains, such as Real-Time Maude, making them amenable to simulation and other kinds of formal analysis. 1

In this paper we propose a method to derive OCL invariants from declarative model-to-model transformations in order to enable their verification and analysis. For this purpose we have defined a number of invariant-based verification properties which provide increasing degrees of confidence about transformation correctness, such as whether a rule (or the whole transformation) is satisfiable by some model, executable or total. We also provide some heuristics for generating meaningful scenarios that can be used to semiautomatically validate the transformations. As a proof of concept, the method is instantiated for two prominent

Abstract. In this paper we propose a method to derive OCL invariants from declarative specifications of model-to-model transformations. In particular we consider two of the most prominent approaches for specifying such transformations: Triple Graph Grammars and QVT. Once the specification is expressed in the form of invariants, the transformation developer can use such description to verify properties of the original transformation (e.g. whether it defines a total, surjective or injective function), and to validate the transformation by the automatic generation of valid pairs of source and target models. 1