A Novel Approach to Semi-automated Evolution of DSML Model Transformation

  • Tihamer Levendovszky
  • Daniel Balasubramanian
  • Anantha Narayanan
  • Gabor Karsai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5969)


In the industrial applications of Model-Based Development, the evolution of modeling languages is an inevitable issue. The migration to the new language involves the reuse of the existing artifacts created for the original language, such as models and model transformations. This paper is devoted to an evolution method for model transformations as well as the related algorithms. The change description is assumed to be available in a modeling language specific to the evolution. Based on the change description, our method is able to automate certain parts of the evolution. When automation is not possible, our algorithms automatically alert the user about the missing semantic information, which can then be provided manually after the automatic part of the interpreter evolution. The algorithms have been implemented and tested in an industrial environment. The results indicate that the semi-automated evolution of model transformations decreases the time and effort required with a manual approach.


Modeling Language Model Transformation Semantic Information Graph Transformation Graph Grammar 
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 2010

Authors and Affiliations

  • Tihamer Levendovszky
    • 1
  • Daniel Balasubramanian
    • 1
  • Anantha Narayanan
    • 1
  • Gabor Karsai
    • 1
  1. 1.Vanderbilt UniversityNashvilleUSA

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