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A Variability-Based Approach to Reusable and Efficient Model Transformations

  • Daniel Strüber
  • Julia Rubin
  • Marsha Chechik
  • Gabriele Taentzer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9033)

Abstract

Large model transformation systems often contain transformation rules that are substantially similar to each other, causing performance bottlenecks for systems in which rules are applied nondeterministically, as long as one of them is applicable. We tackle this problem by introducing variability-based graph transformations. We formally define variability-based rules and contribute a novel match-finding algorithm for applying them. We prove correctness of our approach by showing its equivalence to the classic one of applying the rules individually, and demonstrate the achieved performance speed-up on a realistic transformation scenario.

Keywords

Base Rule Model Transformation Transformation Rule Variation Point Object Constraint Language 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Daniel Strüber
    • 1
  • Julia Rubin
    • 2
  • Marsha Chechik
    • 3
  • Gabriele Taentzer
    • 1
  1. 1.Philipps-Universität MarburgMarburgGermany
  2. 2.Massachusetts Institute of TechnologyCambridgeUSA
  3. 3.University of TorontoTorontoCanada

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