Meta-model Pruning

Sagar Sen 1 Naouel Moha 1 Benoit Baudry 1 Jean-Marc Jézéquel 1
1 TRISKELL - Reliable and efficient component based software engineering
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : Large and complex meta-models such as those of Uml and its profiles are growing due to modelling and inter-operability needs of numerous stakeholders. The complexity of such meta-models has led to coining of the term meta-muddle. Individual users often exercise only a small view of a meta-muddle for tasks ranging from model creation to construction of model transformations. What is the effective meta-model that represents this view? We present a flexible meta-model pruning algorithm and tool to extract effective meta-models from a meta-muddle. We use the notion of model typing for meta-models to verify that the algorithm generates a super-type of the large meta-model representing the meta-muddle. This implies that all programs written using the effective meta-model will work for the meta-muddle hence preserving backward compatibility. All instances of the effective meta-model are also instances of the meta-muddle. We illustrate how pruning the original Uml metamodel produces different effective meta-models.
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Sagar Sen, Naouel Moha, Benoit Baudry, Jean-Marc Jézéquel. Meta-model Pruning. ACM/IEEE 12th International Conference on Model Driven Engineering Languages and Systems (MODELS'09), 2009, Denver, Colorado, USA, United States. ⟨inria-00468514⟩

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