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Cognifying Model-Driven Software Engineering

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Software Technologies: Applications and Foundations (STAF 2017)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 10748))

Abstract

The limited adoption of Model-Driven Software Engineering (MDSE) is due to a variety of social and technical factors, which can be summarized in one: its (real or perceived) benefits do not outweigh its costs. In this vision paper we argue that the cognification of MDSE has the potential to reverse this situation. Cognification is the application of knowledge (inferred from large volumes of information, artificial intelligence or collective intelligence) to boost the performance and impact of a process. We discuss the opportunities and challenges of cognifying MDSE tasks and we describe some potential scenarios where cognification can bring quantifiable and perceivable advantages. And conversely, we also discuss how MDSE techniques themselves can help in the improvement of AI, Machine learning, bot generation and other cognification techniques.

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Notes

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    https://kite.com/.

  2. 2.

    https://www.amazon.com/gp/feature.html?ie=UTF8&docId=1002989731.

  3. 3.

    https://megamart2-ecsel.eu/.

  4. 4.

    http://www.remodd.org/.

  5. 5.

    http://www.mdeforge.org/.

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Author information

Authors and Affiliations

  1. ICREA, Barcelona, Spain

    Jordi Cabot

  2. Universitat Oberta de Catalunya, Barcelona, Spain

    Jordi Cabot & Robert Clarisó

  3. Politecnico di Milano, Milan, Italy

    Marco Brambilla

  4. CEA List, Palaiseau, France

    Sébastien Gérard

Authors
  1. Jordi Cabot
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  2. Robert Clarisó
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  3. Marco Brambilla
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  4. Sébastien Gérard
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Corresponding author

Correspondence to Jordi Cabot .

Editor information

Editors and Affiliations

  1. Johannes Kepler University of Linz, Linz, Austria

    Martina Seidl

  2. Department of Informatics, King’s College London, London, United Kingdom

    Steffen Zschaler

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Cabot, J., Clarisó, R., Brambilla, M., Gérard, S. (2018). Cognifying Model-Driven Software Engineering. In: Seidl, M., Zschaler, S. (eds) Software Technologies: Applications and Foundations. STAF 2017. Lecture Notes in Computer Science(), vol 10748. Springer, Cham. https://doi.org/10.1007/978-3-319-74730-9_13

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  • DOI: https://doi.org/10.1007/978-3-319-74730-9_13

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  • Print ISBN: 978-3-319-74729-3

  • Online ISBN: 978-3-319-74730-9

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