MDE Intelligence

3rd Workshop on Artificial Intelligence and Model-driven Engineering
Co-located with MODELS. October 11, 2021. Fukuoka, Japan Virtual.


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 industry 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 fair and 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, conversational agents 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;
    • AI techniques for 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 and fair AI;
    • Using models for knowledge representation;
    • Code-generation for AI libraries and platforms;
    • Architectural languages for AI-enhanced systems;
    • Model-based testing of AI components.
  • General

    • Tools for combining AI and MDE;
    • Case studies in MDE Intelligence;
    • Experience reports of combining AI and MDE;
    • 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) Research papers: 8 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-blind 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 IEEE MODELS Companion Proceedings.

Papers submitted to MDE Intelligence 2021 must not be under review or submitted for review elsewhere whilst under consideration for MDE intelligence 2021. Contravention of this concurrent submission policy (as stated explicity by the IEEE on will be deemed as a serious breach of scientific ethics, and appropriate action will be taken in all such cases.


  • Paper submission: July 15, 2021
  • Notification: August 21, 2021
  • Camera-ready: August 28, 2021
  • Workshop: October 10-15, 2021


The 3rd edition of the MDE Intelligence workshop will be co-located with MODELS 2021 and aims to discuss current work and challenges at the intersection of MDE and AI. More information about the workshop and the programme of accepted presentations can be found here.

During the workshop, there will be a session of lightning talks around the topics that fall under the scope of the workshop.

We believe that lightning talks are a great opportunity for presenters to promote their work, to receive timely and helpful feedback and to find new collaborations. At the same, these talks will be beneficial for the workshop participants as they may broaden their knowledge and they will be able to actively participate and engage in the discussion around the presented topics.

If you are interested in presenting a lightning talk, please follow the instructions below.

Presenters will have 1-2 minutes to communicate their ideas and they can choose to use up to one slide.

We encourage the submission of proposals around the following topics:

  • Already published works that are relevant for the workshop audience;
  • Discussion of personal visions and ideas;
  • Provocative statements about the past, present or future of the field;
  • Unsolved challenges in academia, industry, open source communities, etc.

      Proposals must be submitted by email to The email should contain the presenter's name and a brief summary of the main idea behind the talk. The deadline for submitting proposals is September 30, 2021.


Monday, 11th October, 2021 (UTC+9) Tokyo time

17:00-17:10 Welcome
17:10-17:50 Keynote by Prof. Sepp Hochreiter
17:50-18:25 Session 1: MDE for AI
  • Viola Wenz, Arno Kesper and Gabriele Taentzer: Detecting Quality Problems in Data Models by Clustering
  • Panagiotis Kourouklidis, Dimitris Kolovos, Joost Noppen and Nicholas Matragkas: A Model-Driven Engineering Approach for Monitoring Machine Learning Models (Vision paper)
18:25-18:35 Coffee break
18:35-19:15 Session 2: Applications
  • Vishnudas Raveendran, Sapan Shah and Sreedhar Reddy: A Model Driven Approach to Building Domain Specific Search Engines
  • Kevin Lano, Sobhan Yassipour-Tehrani and Muhammad Umar: Automated Requirements Formalisation for Agile MDE
19:15-20:00 Lightning talks and Discussion
  • Lightning talk 1. Robbert Jongeling: A research idea for an NLP-assisted recommender for implementation model elements
  • Lightning talk 2. Diego Damasceno, Daniel Strüber: Towards a Catalog of Best Practices for Quality Management of Model-Driven Engineering Research Artifacts
  • Lightning talk 3. Phuong Nguyen: Some thoughts on the application of Deep Learning in Model-Driven Engineering
  • Discussion


  • Biography:

    Sepp Hochreiter is heading the Institute for Machine Learning, the LIT AI Lab and the AUDI.JKU deep learning center at the Johannes Kepler University of Linz, Austria and is director of the Institute of Advanced Research in Artificial Intelligence (IARAI). Sepp Hochreiter is a pioneer of Deep Learning. His contributions the Long Short-Term Memory (LSTM) and the analysis of the vanishing gradient are viewed as milestones and key-moments of the history of both machine learning and Deep Learning. Sepp Hochreiter laid the foundations for Deep Learning in two ways. Dr. Hochreiter's seminal works on the vanishing gradient and the Long Short-Term Memory (LSTM) were the starting points for what became later known as Deep Learning. LSTM has been overwhelmingly successful in handwriting recognition, generation of writings, language modeling and identification, automatic language translation, speech recognition, analysis of audio data, as well as analysis, annotation, and description of video data.

    Sepp Hochreiter is full professor at the Johannes Kepler University, Linz, Austria and head of the Institute for Machine Learning. He is a German citizen, married and has three children.

Sepp Hochreiter




  • Shaukat Ali (Simula Research Laboratory, Norway)
  • Ángela Barriga (Western Norway University of Applied Sciences, Norway)
  • Dominik Bork (TU Wien, Austria)
  • Jessie Carbonnel (Université de Montréal, Canada)
  • Francisco Chicano (University of Málaga, Spain)
  • Ludovico Iovino (Gran Sasso Science Institute, Italy)
  • Lawrence Mandow (University of Málaga, Spain)
  • Shekoufeh Kolahdouz Rahimi (University of Isfahan, Irán)
  • Aurora Ramírez (University of Córdoba, Spain)
  • Bernhard Rumpe (RWTH Aachen University, Germany)
  • Adrian Rutle (Western Norway University of Applied Sciences, Norway)
  • Rijul Saini (McGill, Canada)
  • Matthew Stephan (Miami University, USA)
  • Daniel Strüber (Radboud University Nijmegen, Netherlands)
  • Gabriele Taentzer (Philipps-Universität Marburg, Germany)
  • Marina Tropmann-Frick (Hamburg University of Applied Sciences, Germany)



If you have questions, contact us by email at: