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A unifying framework for homogeneous model composition

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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.

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Notes

  1. Cf. http://thingml.org/.

  2. We are using the subscript \(_{(i)}\) to emphasize that the dots in the event structure represent event occurrences and not event types.

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Authors and Affiliations

  1. McGill University, Montreal, Canada

    Jörg Kienzle & Gunter Mussbacher

  2. University of Toulouse, Toulouse, France

    Benoit Combemale

  3. Universite Cote d’Azur, I3S/INRIA, Nice, France

    Julien Deantoni

Authors
  1. Jörg Kienzle
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  2. Gunter Mussbacher
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  3. Benoit Combemale
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  4. Julien Deantoni
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Correspondence to Benoit Combemale.

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Communicated by Dr Jeff Gray.

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Kienzle, J., Mussbacher, G., Combemale, B. et al. A unifying framework for homogeneous model composition. Softw Syst Model 18, 3005–3023 (2019). https://doi.org/10.1007/s10270-018-00707-8

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