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Component-based Models Going Generic : the MARTE Case-Study

César Olavo de Moura Filho 1 Anne Etien 1 Julien Taillard 1 Cedric Dumoulin 1 Frédéric Guyomarc'H 1
1 DART - Contributions of the Data parallelism to real time
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
Abstract : One of the reasons for using component-based modeling is to improve on reusability. However, there are cases where a whole component cannot be reused just because one element from its internal structure does not present the required features (e.g., type, multiplicity, etc). In this paper, we propose the use of parameterized components as a way to address this problem - and thus to get a further boost on reusability. The UML specification provides support to parameterization via templates. However, when it comes to component-based modeling, UML is but the first metamodel in sometimes long chains of transformations, comprising other domain metamodels. So, in order to keep parameters deeper down the transformation chains, we introduce generic components in those metamodels. However, instead of changing the target metamodel, we decided to create an independent metamodel with the additional concepts required by parameterization, so it can be attached to any target metamodel. The most obvious advantage of this approach is that we do not have to touch the target metamodel. We also demonstrate how existing transformations can be easily adapted to accept the parameter-related concepts. To illustrate our ideas, we used OMG's MARTE metamodel for real-time and embedded systems. The approach has been validated through transformations written in QVT.
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Submitted on : Tuesday, September 9, 2008 - 11:04:05 AM
Last modification on : Tuesday, April 21, 2020 - 12:02:09 PM
Long-term archiving on: : Tuesday, September 21, 2010 - 5:44:59 PM


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  • HAL Id : inria-00319159, version 2



César Olavo de Moura Filho, Anne Etien, Julien Taillard, Cedric Dumoulin, Frédéric Guyomarc'H. Component-based Models Going Generic : the MARTE Case-Study. [Research Report] RR-6632, INRIA. 2008. ⟨inria-00319159v2⟩



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