AbstractIn the field of Model-Driven Engineering, Triple Graph Grammars
(TGGs) play an important role as a rule-based means of implementing
consistency management. From a declarative specification of a
consistency relation, several operations including forward and
backward transformations, (concurrent) synchronisation, and
consistency checks can be automatically derived. For TGGs to be
applicable in realistic application scenarios, expressiveness in
terms of supported language features is very important. A TGG tool
is schema compliant if it can take domain constraints, such as
multiplicity constraints in a meta-model, into account when
performing consistency management tasks. To guarantee schema
compliance, most TGG tools allow application conditions to be
attached as necessary to relevant rules. This strategy is
problematic for at least two reasons: First, ensuring compliance to
a sufficiently expressive schema for all previously mentioned
derived operations is still an open challenge; to the best of our
knowledge, all existing TGG tools only support a very restricted
subset of application conditions. Second, it is conceptually
demanding for the user to indirectly specify domain constraints as
application conditions, especially because this has to be completely
revisited every time the TGG or domain constraint is changed. While
domain constraints can in theory be automatically transformed to
obtain the required set of application conditions, this has only
been successfully transferred to TGGs for a very limited subset of
domain constraints. To address these limitations, this paper
proposes a search-based strategy for achieving schema compliance. We
show that all correctness and completeness properties, previously
proven in a setting without domain constraints, still hold when
schema compliance is to be additionally guaranteed. An
implementation and experimental evaluation are provided to support
our claim of practical applicability.
Bridging the Gap between Modeling of Mobile Agent-based Systems and Semantic Web using Meta-Modeling and Graph Grammars