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This paper provides a systematic evaluation framework for comparing model transformation approaches, based upon the ISO/IEC 9126-1 quality characteristics for software systems.
This paper compares several model-to-model transformation languages as a step in the direction of gathering knowledge about the existing model transformation approaches.
In parallel with the QVT activity, many research groups and companies have been working on their own model transformation approaches and languages.
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Finally, the SysML-based system dynamic behavior, and the related simulation information are integrated with the platform-specific simulation model through a bidirectional model transformation approach based on a triple graph grammar (TGG), which facilitates the automatic model consistency and traceability between system design and simulation.
For each one of the constituent types, a specific type of stimuli generator was automatically produced using our model transformation approach.
Therefore, we foster a model-driven transformation approach from modelled security goals in the context of process models to concrete security implementations.
In this paper will be described transformation approaches model-to-model and model-to-text.
Based on Lyapunov Krasovskii functional, the descriptor model transformation and LMI approach, new sufficient conditions for solvability of this problem are obtained.
They reflect different topics of enterprise modeling including quality of models, change management and transformation, approaches and tools for agility and flexibility, enterprise modeling and business processes, enterprise modeling and information systems and enterprise modeling cases.
By utilizing the model transformation idea, an input output approach is employed to transform the time-delayed filtering error system into a feedback interconnection formulation.
Applying a model-to-model transformation, our approach integrates platform-specific performance influences of the underlying middleware while enabling the use of different existing analytical and simulation-based prediction techniques.
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