Abstract
While comparing different model transformation languages (MTLs), it is common to refer to their syntactic and semantic features and overlook their supporting tools’ performance. Performance is one of the aspects that can hamper the application of MDD to industrial scenarios. An highly declarative MTL might simply not scale well when using large models due to its supporting implementation. In this paper, we focus on the several pattern matching techniques (including optimization techniques) employed in the most popular transformation tools, and discuss their effectiveness w.r.t. the expressive power of the languages used. Because pattern matching is the most costly operation in a transformation execution, we present a classification of the existing model transformation tools according to the pattern matching optimization techniques they implement. Our classification complements existing ones that are more focused at syntactic and semantic features of the languages supported by those tools.
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