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Title: Constraint programming for type inference in flexible model-driven engineering
Author: Zolotas, Athanasios
Clarisó Viladrosa, Robert  
Matragkas, Nicholas
Kolovos, Dimitrios S.
Paige, Richard F.
Others: University of York
Universitat Oberta de Catalunya. Estudis d'Informàtica, Multimèdia i Telecomunicació
University of Hull
Keywords: flexible modelling
bottom-up modelling
type inference
constraint programming
example-driven modelling
Issue Date: 11-Dec-2016
Publisher: Computer Languages, Systems and Structures
Citation: Zolotas, A., Clarisó, R., Matragkas, N., Kolovos, D. & Paige, R. (2017). Constraint programming for type inference in flexible model-driven engineering. Computer Languages, Systems and Structures, 49, 216-230. doi: 10.1016/
Project identifier: info:eu-repo/grantAgreement/#611125
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Abstract: 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.
Language: English
ISSN: 1477-8424
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