MDE Intelligence

2nd Workshop on Artificial Intelligence and Model-driven Engineering
Co-located with MODELS. 16 October 2020. Montreal, Canada Virtual.


COVID-19 Impact

Given the current global situation of the pandemic and the restrictions regarding traveling and organizing events, the MODELS Steering Committee and Organizing Committee have made the decision to celebrate MODELS and all its associated events (including the MDE Intelligence workshop) virtually this year. This will not affect the publication of all accepted papers since ACM has already confirmed that they will be published in the proceedings of the conference. More information will be made available soon.

Theme & Goals

Artificial Intelligence (AI) has become part of everyone's life. It is used by companies to exploit the information they collect to improve the products and/or services they offer and, wanted or unwanted, it is present in almost every device around us. Lately, AI is also starting to impact all aspects of the system and software development lifecycle, from their upfront specification to their design, testing, deployment and maintenance, with the main goal of helping engineers produce systems and software faster and with better quality while being able to handle ever more complex systems. The hope is that AI will help dealing with the increasing complexity of systems and software.

There is no doubt that MDE has been a means to tame until now part of this complexity. However, its adoption by industrial still relies on their capacity to manage the underlying methodological changes including among other things the adoption of new tools. To go one step further, we believe there is a clear need for AI-empowered MDE, which will push the limits of "classic" MDE and provide the right techniques to develop the next generation of highly complex model-based system and software systems engineers will have to design tomorrow.

This workshop provides a forum to discuss, study and explore the opportunities and challenges raised by the integration of AI and MDE.

We would like to address topics such as how to choose, evaluate and adapt AI techniques to Model-Driven Engineering as a way to improve current system and software modeling and generation processes in order to increase the benefits and reduce the costs of adopting MDE. We believe that AI artifacts will empower the MDE tools and boost hence the advantages, and then adoption, of MDE at industry level.

At the same time, AI is software (and complex software, in fact), we also believe that such AI-powered MDE approach will also benefit the design of AI artifacts themselves and specially to face the challenge of designing "trustable" AI software.

Last but not least, although AI is the most popular branch of computer science to create and simulate intelligence, we also believe that any kind of technique that provides human cognitive capabilities and helps creating "intelligent" software are also in the scope of this workshop. An example would be the knowledge representation techniques and ontologies that can be useful on its own or support other kinds of AI techniques.

Call for Papers

Model-driven engineering (MDE) and artificial intelligence (AI) are two separate fields in computer science, which can clearly benefit from cross-pollination and collaboration. There are at least two ways in which such integration—which we call MDE Intelligence—can manifest:

  • Artificial Intelligence for MDE. MDE can benefit from integrating AI concepts and ideas to increase its power: flexibility, user experience, quality, etc. For example, using model transformations through search-based approaches, or by increasing the ability to abstract from partially formed, manual sketches into fully-shaped and formally specified meta-models and editors.
  • MDE for Artificial Intelligence. AI is software, and as such, it can benefit from integrating concepts and ideas from MDE that have been proven to improve software development. For example, using domain-specific languages allows domain experts to directly express and manipulate their problems while providing an auditable conversion pipeline. Together this can improve trust in and safety of AI technologies. Similarly, MDE technologies can contribute to the goal of explainable AI.


Topics of interest for the workshop include, but are not limited to:

  • AI for MDE

    • Application of (meta-heuristic) search to modelling problems;
    • Machine learning of models, meta-models, concrete syntax, model transformations, etc.;
    • AI planning applied to modelling, meta-modelling, and model management;
    • Modeling assistants such as bots, chatbots and virtual assistants/recommenders supporting diverse modeling tasks;
    • Model inferencers and automatic model generators from datasets;
    • Self-adapting code generators;
    • AI-based user interface adaptation for modeling tools;
    • AI with human-in-the-loop for modeling;
    • Semantic reasoning platforms over domain-specific models;
    • Semantic integration of design-time models with runtime data;
    • General-knowledge or domain-specific ontologies;
    • Probabilistic models;
    • Use of AI techniques in data, process and model mining and categorisation;
    • Natural language processing applied to modelling;
    • Perception and modeling.
  • MDE for AI

    • Domain-specific modelling approaches for AI planning, machine learning, agent-based modelling, etc.;
    • Model-driven processes for AI system development;
    • MDE techniques for explainable AI;
    • Using models for knowledge representation;
    • Code-generation for AI libraries and platforms;
    • Model-based testing of AI components.
  • General

    • Tools for combining AI and MDE;
    • Case studies in MDE Intelligence;
    • Challenge problems to be addressed by combining AI and MDE techniques.


Papers will follow the same formatting guidelines as the main tracks of the conference (please check them here). We ask for two type of contributions:
  • 1) Work-in-progress papers: 10 pages,
  • 2) Vision papers, experience papers or demos: 5 pages.
Submissions must be uploaded through EasyChair in the following link .

All submissions will follow a single-bling review process where each paper will be reviewed by at least 3 members of the program committee. They will value the relevance and interest for discussions that will take place at the workshop. Accepted papers will be published in the ACM Satellite Event Proceedings.

Papers submitted to MDE Intelligence 2020 must not be under review or submitted for review elsewhere whilst under consideration for MDE intelligence 2020. Contravention of this concurrent submission policy (as stated explicity by the ACM on will be deemed as a serious breach of scientific ethics, and appropriate action will be taken in all such cases.


  • Abstract submission: July 15, 2020
  • Paper submission: July 22, 2020 July 26, 2020 (strict)
  • Notification: August 21, 2020
  • Camera-ready: August 28, 2020
  • Workshop: October 16, 2020


Friday 16th October, 2020 (GMT-04:00) Eastern Time (US and Canada)

7.00-8.30 Welcome and Paper Presentations
  • Welcome
  • Towards an Assessment Grid for Intelligent Modeling Assistance. Gunter Mussbacher, Benoit Combemale, Silvia Abrahão, Nelly Bencomo, Loli Burgueño, Gregor Engels, Jörg Kienzle, Thomas Kühn, Sebastien Mosser, Houari Sahraoui and Martin Weyssow.
  • DoMoBOT: A Bot for Automated and Interactive Domain Modelling. Rijul Saini, Gunter Mussbacher, Jin L.C. Guo and Jörg Kienzle
  • A comparative study of reinforcement learning techniques to repair models. Angela Barriga, Lawrence Mandow, José Luis Pérez de la Cruz, Adrian Rutle, Rogardt Heldal and Ludovico Iovino
  • Enhancing model transformation synthesis using natural language processing. Kevin Lano, Shichao Fang and Sobhan Yassipour Tehrani
8.30-9.00 Coffee break
9.00-9.45 Keynote
  • Speaker: Alexander Egyed
  • Title: Machine Learning and Software Engineering: Where are we?
  • Abstract: AI-enabled software is already ubiquitous in every walk of life and at the forefront of the disruptive technologies of the future. AI-enabled tools alone are expected to generate $2.9 trillion in business value by 2021 and, at least, 40 percent of new application development projects will have AI co-developers on their team by 2022. Is the software engineering community ready? This talk investigates Machine Learning (ML), undoubtedly the most hyped part of AI, and how it is changing our software engineering practices.
  • Bio: Alexander Egyed is Professor for Software-Intensive Systems at the Johannes Kepler University, Austria. He received his Doctorate from the University of Southern California, USA and worked in industry for many years. He is most recognized for his work on software and systems design – particularly on variability, consistency, and traceability.
Alexander Eyged
9.45-10.30 Discussion




  • Gregor Engels (University of Paderborn)
  • Moharram Challenger (University of Antwerp)
  • Shekoufeh Kolahdouz Rahimi (University of Isfahan)
  • Aurora Ramírez (University of Córdoba)
  • Gunter Mussbacher (McGill University)
  • Shaukat Ali (Simula Research Laboratory)
  • Nelly Bencomo (Aston University)
  • Francisco Chicano (University of Málaga)
  • Daniel Strüber (Chalmers University & University of Gothenburg)
  • Adrian Rutle (Western Norway University of Applied Sciences)
  • Daniel Varro (McGill University & Budapest University of Technology and Economics)
  • Gabriele Taentzer (Philipps-Universität Marburg)
  • Bernhard Rumpe (RWTH Aachen University)
  • Betty Cheng (Michigan State University)
  • Shuai Li (CEA LIST)
  • Sandeep Neema (Vanderbilt University)



If you have questions, contact us by email at: