Abstract
Domain-specific modelling languages, in particular those described in terms of UML profiles, use graphical notations to maximise human understanding and facilitate communication among stakeholders. Nevertheless, textual notations are preferred for specific purposes, due to the nature of a specific domain, or for personal preference. The mutually exclusive use of graphical or textual modelling is not sufficient for the development of complex systems developed by large heterogeneous teams. We envision a modern modelling framework supporting seamless hybrid graphical and textual modelling. Such a framework would provide several benefits, among which: flexible separation of concerns, multi-view modelling based on multiple notations, convenient text-based editing operations, and text-based model editing outside the modelling environment, and faster modelling activities.
In this paper we describe our work towards such a framework for UML and profiles. The uniqueness is that both graphical and textual modelling are done on a common persistent model resource, thus dramatically reducing the need for synchronisation among the two notations.
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Notes
- 1.
The interested reader can refer to the Xtext specification [24] for further details about the overall inference process.
- 2.
The Xtext language for state-machines is not in the scope of this paper and was created for experimental purposes only.
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Acknowledgements
We would like to thank Simon Redding, Francis Bordeleau, and Matthias Tichy for the fruitful discussions and support. This work is partially supported by the Papyrus Industry Consortium(https://wiki.polarsys.org/Papyrus_IC), the EUREKA network Hybrid Modeling project(http://www.eurekanetwork.org/project/id/10700), and the KK-foundation MOMENTUM project(http://www.es.mdh.se/projects/458-MOMENTUM).
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Addazi, L., Ciccozzi, F., Langer, P., Posse, E. (2017). Towards Seamless Hybrid Graphical–Textual Modelling for UML and Profiles. In: Anjorin, A., Espinoza, H. (eds) Modelling Foundations and Applications. ECMFA 2017. Lecture Notes in Computer Science(), vol 10376. Springer, Cham. https://doi.org/10.1007/978-3-319-61482-3_2
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