The design process of complex systems requires a precise checking of the functional and dependability attributes of the target design. The growing complexity of systems necessitates the use of formal methods, as the exhaustiveness of checks performed by the traditional simulation and testing is insu cient. For this reason, the mathematical models of various formal veriÿcation tools are automatically derived from UML-diagrams of the model by mathematical transformations guaranteeing a complete consistency between the target design and the models of veriÿcation and validation tools. In the current paper, a general framework for an automated model transformation system is presented. The method starts from a uniform visual description and a formal proof concept of the particular transformations by integrating the powerful computational paradigm of graph transformation, planner algorithms of artiÿcial intelligence, and various concepts of computer engineering.
Incremental pattern matching is a key challenge for many tool integration, model synchronization and (discrete-event) model simulation tasks. An incremental pattern matching engine explicitly stores existing matches, while these matches are maintained incrementally with respect to the changes of the underlying model. In the current paper, we present an adaptation of RETE networks [6] in order to provide incremental support for the transformation language of the VIATRA2 framework. We evaluate the performance of the incremental engine on a benchmark problem assessing the speedup of incremental processing in the case of as-long-as-possible type of rule applications.
The current paper makes two contributions for the graph pattern matching problem of model transformation tools. First, model-sensitive search plan generation is proposed for pattern traversal (as an extension to traditional multiplicity and type considerations of existing tools) by estimating the expected performance of search plans on typical instance models that are available at transformation design time. Then, an adaptive approach for graph pattern matching is presented, where the optimal search plan can be selected from previously generated search plans at run-time based on statistical data collected from the current instance model under transformation.
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