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

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.

Domaines: Génie logiciel [cs.SE]

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⟩