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.
https://hal.inria.fr/inria-00468514
Contributor : Didier Vojtisek
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Submitted on : Wednesday, March 31, 2010 - 9:28:16 AM
Last modification on : Friday, November 16, 2018 - 1:28:21 AM
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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⟩
