Anzeige
Erschienen in:
2009 | OriginalPaper | Buchkapitel
Meta-model Pruning
verfasst von : Sagar Sen, Naouel Moha, Benoit Baudry, Jean-Marc Jézéquel
Erschienen in: Model Driven Engineering Languages and Systems
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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
meta-model produces different effective meta-models.