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Opportunities in intelligent modeling assistance

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Abstract

Modeling is requiring increasingly larger efforts while becoming indispensable given the complexity of the problems we are solving. Modelers face high cognitive load to understand a multitude of complex abstractions and their relationships. There is an urgent need to better support tool builders to ultimately provide modelers with intelligent modeling assistance that learns from previous modeling experiences, automatically derives modeling knowledge, and provides context-aware assistance. However, current intelligent modeling assistants (IMAs) lack adaptability and flexibility for tool builders, and do not facilitate understanding the differences and commonalities of IMAs for modelers. Such a patchwork of limited IMAs is a lost opportunity to provide modelers with better support for the creative and rigorous aspects of software engineering. In this expert voice, we present a conceptual reference framework (RF-IMA) and its properties to identify the foundations for intelligent modeling assistance. For tool builders, RF-IMA aims to help build IMAs more systematically. For modelers, RF-IMA aims to facilitate comprehension, comparison, and integration of IMAs, and ultimately to provide more intelligent support. We envision a momentum in the modeling community that leads to the implementation of RF-IMA and consequently future IMAs. We identify open challenges that need to be addressed to realize the opportunities provided by intelligent modeling assistance.

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Author information

Authors and Affiliations

  1. McGill University, Montreal, Canada

    Gunter Mussbacher, Jörg Kienzle, Hyacinth Ali, Márton Búr & Dániel Varró

  2. Université de Toulouse and Inria, Toulouse, France

    Benoit Combemale

  3. Universitat Politècnica de València, Valencia, Spain

    Silvia Abrahão

  4. Aston University, Birmingham, UK

    Nelly Bencomo

  5. Universitat Oberta de Catalunya, Barcelona, Spain

    Loli Burgueño

  6. CEA LIST, Palaiseau, France

    Loli Burgueño

  7. Paderborn University, Paderborn, Germany

    Gregor Engels

  8. Inria, CNRS, IRISA, Université de Rennes, Rennes, France

    Pierre Jeanjean & Jean-Marc Jézéquel

  9. Karlsruher Institut für Technologie, Karlsruhe, Germany

    Thomas Kühn

  10. Université du Québec à Montréal, Montreal, Canada

    Sébastien Mosser

  11. Université de Montréal, Montreal, Canada

    Houari Sahraoui, Eugene Syriani & Martin Weyssow

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  1. Gunter Mussbacher
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Mussbacher, G., Combemale, B., Kienzle, J. et al. Opportunities in intelligent modeling assistance. Softw Syst Model 19, 1045–1053 (2020). https://doi.org/10.1007/s10270-020-00814-5

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