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A unifying framework for homogeneous model composition

  • Jörg Kienzle
  • Gunter Mussbacher
  • Benoit Combemale
  • Julien Deantoni
Regular Paper
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Abstract

The growing use of models for separating concerns in complex systems has lead to a proliferation of model composition operators. These composition operators have traditionally been defined from scratch following various approaches differing in formality, level of detail, chosen paradigm, and styles. Due to the lack of proper foundations for defining model composition (concepts, abstractions, or frameworks), it is difficult to compare or reuse composition operators. In this paper, we stipulate the existence of a unifying framework that reduces all structural composition operators to structural merging, and all composition operators acting on discrete behaviors to event scheduling. We provide convincing evidence of this hypothesis by discussing how structural and behavioral homogeneous model composition operators (i.e., weavers) can be mapped onto this framework. Based on this discussion, we propose a conceptual model of the framework and identify a set of research challenges, which, if addressed, lead to the realization of this framework to support rigorous and efficient engineering of model composition operators for homogeneous and eventually heterogeneous modeling languages.

Keywords

Model composition Symmetric merge Event scheduling Event structures Separation of concerns 

Notes

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Jörg Kienzle
    • 1
  • Gunter Mussbacher
    • 1
  • Benoit Combemale
    • 2
  • Julien Deantoni
    • 3
  1. 1.McGill UniversityMontrealCanada
  2. 2.University of ToulouseToulouseFrance
  3. 3.Universite Cote d’Azur, I3S/INRIANiceFrance

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