2011
DOI: 10.1007/978-3-642-24485-8_46
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Towards a General Composition Semantics for Rule-Based Model Transformation

Abstract: Abstract. As model transformations have become an integral part of the automated software engineering lifecycle, reuse, modularisation, and composition of model transformations becomes important. One way to compose model transformations is to compose modules of transformation rules, and execute the composition as one transformation (internal composition). This kind of composition can provide fine-grained semantics, as it is part of the transformation language. This paper aims to generalise two internal composi… Show more

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Cited by 38 publications
(34 citation statements)
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“…Generic low-level transformation engines include EMFTVM [155] or T-Core [124]. SIGMA [91] provides Scala-embedded DSLs that map Epsilon model transformation functionality directly into Scala as a specification language, and the JVM as the execution platform.…”
Section: Fig 10 a Reactive Transformation Rulementioning
confidence: 99%
“…Generic low-level transformation engines include EMFTVM [155] or T-Core [124]. SIGMA [91] provides Scala-embedded DSLs that map Epsilon model transformation functionality directly into Scala as a specification language, and the JVM as the execution platform.…”
Section: Fig 10 a Reactive Transformation Rulementioning
confidence: 99%
“…The Integrated Transformation Environment (ITE) [24] allows users to use many transformation engines in the same environment. [14] proposes a common virtual machine to have different model-transformation languages interoperate on EMF. A general schema for the migration of a model transformation engine to a different platform has been investigated in [25], together with a practical experimentation migrating ATL from EMF to Microsoft DSL Tools.…”
Section: Related Workmentioning
confidence: 99%
“…The model transformation is made using ATL 4 , with the EMF Transformation Virtual Machine (EMFTVM). Indeed, besides including all the standard ATL VM features, EMFTVM offers better model loading and saving, smaller file results, and an overall performance improvement during the ATL transformation [12].…”
Section: Model Transformationmentioning
confidence: 99%