A Comparison of Rule Inheritance in Model-to-Model Transformation Languages

Manuel Wimmer, Gertrude Kappel, Angelika Kusel, Werner Retschitzegger, Johannes Schönböck, Wieland Schwinger, Dimitris S. Kolovos, Richard F. Paige, Marius Lauder, Andy Schürr, Dennis Wagelaar

Research output: Chapter in Book/Report/Conference proceedingConference proceedingspeer-review

Abstract

Although model transformations presumably play a major role in Model-Driven Engineering, reuse mechanisms such as inheritance have received little attention so far. In this paper, we propose a comparison framework for rule inheritance in declarative model-to-model transformation languages, and provide an in-depth evaluation of three prominent representatives thereof, namely ATL, ETL (declarative subsets thereof), and TGGs. The framework provides criteria for comparison along orthogonal dimensions, covering static aspects, which indicate whether a set of inheriting transformation rules is well-formed at compiletime, and dynamic aspects, which describe how inheriting rules behave at run-time. The application of this framework to dedicated transformation languages shows that, while providing similar syntactical inheritance concepts, they exhibit different dynamic inheritance semantics and offer basic support for checking static inheritance semantics, only.
Original languageEnglish
Title of host publicationProceedings of the 4th International Conference on Model Transformation (ICMT 2011)
Number of pages15
Publication statusPublished - 2011

Fields of science

  • 106013 Genetics
  • 106041 Structural biology
  • 102 Computer Sciences
  • 101029 Mathematical statistics
  • 102001 Artificial intelligence
  • 101004 Biomathematics
  • 102015 Information systems
  • 102018 Artificial neural networks
  • 106002 Biochemistry
  • 106023 Molecular biology
  • 305 Other Human Medicine, Health Sciences
  • 106005 Bioinformatics

JKU Focus areas

  • Computation in Informatics and Mathematics
  • Nano-, Bio- and Polymer-Systems: From Structure to Function

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