Incremental (Unidirectional) Model Transformation with eMoflon::IBeX

  • Nils WeidmannEmail author
  • Anthony Anjorin
  • Patrick Robrecht
  • Gergely Varró
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11629)


Graph transformation is a mature formalism often used as a basis for model transformation tools. Although numerous graph transformation tools exist, very few explore the paradigm of reactive, event-driven programming via incremental graph transformation. As we believe reactive programming to be a promising application for graph transformation in both research and teaching, we have developed eMoflon::IBeX as a suitable environment for incremental unidirectional model transformation via graph transformation. With eMoflon::IBeX, we have realised a novel mix of complementary tool features that have proven to be useful and effective in predecessor tools. We discuss these features and present insights based on an empirical evaluation of eMoflon::IBeX.


Graph transformation Incremental pattern matching 


  1. 1.
    Anjorin, A., Lauder, M., Patzina, S., Schürr, A.: eMoflon: leveraging EMF and professional CASE tools. In: Informatik 2011, p. 281 (2011)Google Scholar
  2. 2.
    Anjorin, A., Leblebici, E., Schürr, A.: 20 years of triple graph grammars: a roadmap for future research. ECEASST 73 (2015)Google Scholar
  3. 3.
    Anjorin, A., Robrecht, P.: Unidirectional model transformation with eMoflon::IBeX (2018).
  4. 4.
    Bergmann, G., Ráth, I., Varró, G., Varró, D.: Change-driven model transformations. SoSyM 11(3), 431–461 (2012)Google Scholar
  5. 5.
    Beyhl, T., Giese, H.: Incremental view maintenance for deductive graph databases using generalized discrimination networks. In: Heußner, A., Kissinger, A., Wijs, A. (eds.) GaM@ETAPS 2016. EPTCS, vol. 231, pp. 57–71 (2016)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Biermann, E., Ermel, C., Taentzer, G.: Formal foundation of consistent EMF model transformations by algebraic graph transformation. SoSyM 11(2), 227–250 (2012)Google Scholar
  7. 7.
    Fritsche, L., Kosiol, J., Schürr, A., Taentzer, G.: Efficient model synchronization by automatically constructed repair processes. In: Hähnle, R., van der Aalst, W. (eds.) FASE 2019. LNCS, vol. 11424, pp. 116–133. Springer, Cham (2019). Scholar
  8. 8.
    Klar, F., Königs, A., Schürr, A.: Model transformation in the large. In: ESEC-FSE 2007, pp. 285–294. ACM, New York (2007)Google Scholar
  9. 9.
    Klassen, L., Wagner, R.: EMorF - a tool for model transformations. ECEASST 54 (2012)Google Scholar
  10. 10.
    Kluge, R., Stein, M., Giessing, D., Schürr, A., Mühlhäuser, M.: cMoflon: model-driven generation of embedded C code for wireless sensor networks. In: Anjorin, A., Espinoza, H. (eds.) ECMFA 2017. LNCS, vol. 10376, pp. 109–125. Springer, Cham (2017). Scholar
  11. 11.
    Leblebici, E., Anjorin, A., Fritsche, L., Varró, G., Schürr, A.: Leveraging incremental pattern matching techniques for model synchronisation. In: de Lara, J., Plump, D. (eds.) ICGT 2017. LNCS, vol. 10373, pp. 179–195. Springer, Cham (2017). Scholar
  12. 12.
    Leblebici, E., Anjorin, A., Schürr, A.: Developing eMoflon with eMoflon. In: Di Ruscio, D., Varró, D. (eds.) ICMT 2014. LNCS, vol. 8568, pp. 138–145. Springer, Cham (2014). Scholar
  13. 13.
    Perez, S.M., Tisi, M., Douence, R.: Reactive model transformation with ATL. Sci. Comput. Program. 136, 1–16 (2017)CrossRefGoogle Scholar
  14. 14.
    Schneider, S., Lambers, L., Orejas, F.: A logic-based incremental approach to graph repair. In: Hähnle, R., van der Aalst, W. (eds.) FASE 2019. LNCS, vol. 11424, pp. 151–167. Springer, Cham (2019). Scholar
  15. 15.
    Varró, D., Bergmann, G., Hegedüs, Á., Horváth, Á., Ráth, I., Ujhelyi, Z.: Road to a reactive and incremental model transformation platform: three generations of the VIATRA framework. SoSyM 15(3), 609–629 (2016)Google Scholar
  16. 16.
    Varró, G., Anjorin, A., Schürr, A.: Unification of compiled and interpreter-based pattern matching techniques. In: Vallecillo, A., Tolvanen, J.-P., Kindler, E., Störrle, H., Kolovos, D. (eds.) ECMFA 2012. LNCS, vol. 7349, pp. 368–383. Springer, Heidelberg (2012). Scholar
  17. 17.
    Varró, G., Deckwerth, F.: A rete network construction algorithm for incremental pattern matching. In: Duddy, K., Kappel, G. (eds.) ICMT 2013. LNCS, vol. 7909, pp. 125–140. Springer, Heidelberg (2013). Scholar
  18. 18.
    Weber, J.H.: GRAPE – a graph rewriting and persistence engine. In: de Lara, J., Plump, D. (eds.) ICGT 2017. LNCS, vol. 10373, pp. 209–220. Springer, Cham (2017). Scholar
  19. 19.
    Yigitbas, E., Anjorin, A., Leblebici, E., Grieger, M.: Bidirectional method patterns for language editor migration. In: Pierantonio, A., Trujillo, S. (eds.) ECMFA 2018. LNCS, vol. 10890, pp. 97–114. Springer, Cham (2018). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nils Weidmann
    • 1
    Email author
  • Anthony Anjorin
    • 1
  • Patrick Robrecht
    • 2
  • Gergely Varró
    • 2
  1. 1.Paderborn UniversityPaderbornGermany
  2. 2.PaderbornGermany

Personalised recommendations