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A framework for efficient model transformations
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 10.61 MB | Adobe PDF |
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Orientador(es)
Resumo(s)
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 oriented
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.
Descrição
Palavras-chave
Model transformations DSL Language design Pattern matching Model transformation optimization Model-driven development