The intensive use of models in model-driven engineering (MDE) raises the need to develop meta-models with different aims, such as the construction of textual and visual modelling languages and the specification of sou...
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The intensive use of models in model-driven engineering (MDE) raises the need to develop meta-models with different aims, such as the construction of textual and visual modelling languages and the specification of source and target ends of model-to-model transformations. While domain experts have the knowledge about the concepts of the domain, they usually lack the skills to build meta-models. Moreover, meta-models typically need to be tailored according to their future usage and specific implementation platform, which demands knowledge available only to engineers with great expertise in specific MDE platforms. These issues hinder a wider adoption of MDE both by domain experts and software engineers. In order to alleviate this situation, we propose an interactive, iterative approach to meta-model construction, enabling the specification of example model fragments by domain experts, with the possibility of using informal drawing tools like Dia or yED. These fragments can be annotated with hints about the intention or needs for certain elements. A meta-model is then automatically induced, which can be refactored in an interactive way, and then compiled into an implementation meta-model using profiles and patterns for different platforms and purposes. Our approach includes the use of a virtual assistant, which provides suggestions for improving the meta-model based on well-known refactorings, and a validation mode, enabling the validation of the meta-model by means of examples.
Domain experts typically have detailed knowledge of the concepts that are used in their domain;however they often lack the technical skills needed to translate that knowledge into model-driven engineering (MDE) idioms...
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Domain experts typically have detailed knowledge of the concepts that are used in their domain;however they often lack the technical skills needed to translate that knowledge into model-driven engineering (MDE) idioms and technologies. Flexible or bottom-up modelling has been introduced to assist with the involvement of domain experts by promoting the use of simple drawing tools. In traditional MDE the engineering process starts with the definition of a metamodel which is used for the instantiation of models. In bottom-up MDE example models are defined at the beginning, letting the domain experts and language engineers focus on expressing the concepts rather than spending time on technical details of the metamodelling infrastructure. The metamodel is then created manually or inferred automatically. The flexibility that bottom-up MDE offers comes with the cost of having nodes in the example models left untyped. As a result, concepts that might be important for the definition of the domain will be ignored while the example models cannot be adequately re-used in future iterations of the language definition process. In this paper, we propose a novel approach that assists in the inference of the types of untyped model elements using Constraint Programming. We evaluate the proposed approach in a number of example models to identify the performance of the prediction mechanism and the benefits it offers. The reduction in the effort needed to complete the missing types reaches up to 91.45% compared to the scenario where the language engineers had to identify and complete the types without guidance. (C) 2016 The Authors. Published by Elsevier Ltd.
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