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Classification of Model Transformation Tools: Pattern Matching Techniques

  • Conference paper
Model-Driven Engineering Languages and Systems (MODELS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 8767))

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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Author information

Authors and Affiliations

  1. CITI, Departamento de Informática, Faculdade de Ciências e Tecnologias, Universidade Nova de Lisboa, Portugal

    Cláudio Gomes, Bruno Barroca & Vasco Amaral

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  1. Cláudio Gomes
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  2. Bruno Barroca
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  3. Vasco Amaral
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Editor information

Editors and Affiliations

  1. Queen’s University, Kingston, ON, Canada

    Juergen Dingel

  2. Microsoft Research, Redmond, WA, USA

    Wolfram Schulte

  3. Universitat Politècnica de València, Spain

    Isidro Ramos , Silvia Abrahão  & Emilio Insfran ,  & 

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Gomes, C., Barroca, B., Amaral, V. (2014). Classification of Model Transformation Tools: Pattern Matching Techniques. In: Dingel, J., Schulte, W., Ramos, I., Abrahão, S., Insfran, E. (eds) Model-Driven Engineering Languages and Systems. MODELS 2014. Lecture Notes in Computer Science, vol 8767. Springer, Cham. https://doi.org/10.1007/978-3-319-11653-2_38

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  • DOI: https://doi.org/10.1007/978-3-319-11653-2_38

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11652-5

  • Online ISBN: 978-3-319-11653-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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