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A Framework for Efficient Model Transformation
MSc Post-Graduation

The reported productivity gains while using models and model transformations to develop entire systems, after almost a decade of experience applying model-driven approaches for system development, are already undeniable benefits of this approach. However, the slowness of higher-level, rule based model transformation languages hinders the applicability of this approach to industrial scales. Lower-level, and efficient, languages can be used but productivity and easy maintenance seize to exist. The abstraction penalty problem is not new, it also exists for high-level, object ori- ented languages but everyone is using them now. Why is not everyone using rule based model transformation languages then? In this thesis, we propose a framework, comprised of a language and its respective environment, designed to tackle the most performance critical operation of high-level model transformation languages: the pattern matching. This framework shows that it is possible to mitigate the performance penalty while still using high-level model transformation languages.

Start Date: 2012-09-01

End Date: 2013-12-20

Post-Graduation Student / Researcher / Professor:
  • Cláudio Gomes ( Departamento de Informática FCT/UNL )

Post-Graduation Supervisor(s):

Post-Graduation Jury:
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