MDE Intelligence

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 impacting 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 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

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

• AI for MDE

  • Application of large language models (LLMs), Generative AI and machine learning to modelling problems;
  • Machine learning and Generative AI for (meta-heuristic) search (meta)models, concrete syntax, model transformations, etc.;
  • AI planning applied to (meta-)modelling, and model management;
  • AI-supported modelling (e.g., bots, recommenders, UI adaptation, etc.)
  • Model inferencers and automatic, dataset-based model generators;
  • Self-adapting code generators;
  • Semantic reasoning, knowledge graphs or domain-specific ontologies;
  • AI-supported model-based digital twins;
  • Probabilistic, descriptive or predictive models;
  • AI techniques for data, process and model mining and categorisation;
  • Natural language processing applied to modeling, including Large Language Models (LLM) and Generative AI;
  • Data quality and privacy issues in AI for MDE;
  • Reinforcement learning to optimize modelling tasks.

• 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;
  • MDE for federated learning;
  • Model-based testing/analysis of AI components.

• General

  • AI in teaching MDE;
  • AI for MDE UX;
  • Tools, frameworks, modeling standards;
  • Experience reports, case studies, and empirical studies;
  • Challenges.

SUBMISSIONS

• 1) Research papers: 8 pages,
• 2) Vision papers, experience papers or demos: 5 pages.

IMPORTANT DATES

• Paper submission: July 5, 2024
• Notification: August 7, 2024
• Camera-ready: August 16, 2024
• Workshop: September, 2024

CALL FOR LIGHTNING TALKS

The 6th edition of the MDE Intelligence workshop will be co-located with MODELS 2024 and aims to discuss current work and challenges at the intersection of MDE and AI.

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 2-3 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.

PROPOSAL SUBMISSION

Proposals must be submitted using the submission form. The deadline for submitting proposals is September 20, 2024.

ACCEPTED LIGHTNING TALKS

• Abstraction Engineering (BoF) – Andrzej Wąsowski
• Model quality in the era of AI (BoF) – Jose Antonio Hernandez
• Benchmarking of LLMs in software modelling tasks – Lola Burgueño
• Automated Assessment of Domain Models – Boqi Chen
• Towards Synthetic Trace Generation of Modeling Operations using In-Context Learning Approach – Claudio di Sipio
• One-shot learning for creating models conforming to restrictive textual grammars – Nathan Hagel
• Cross Domain Interoperability and Consistency in Heterogeneous Systems – Joshua Tetteh Ocansey

Committees

ORGANIZING COMMITTEE

Lola Burgueño
Univ. of Malaga, Spain
@LolaBurguenoTwitter

Dominik Bork
TU Wien, Austria
@BorkDominikTwitter

Jordi Cabot
LIST, Luxembourg
@JordiCabotTwitter

Sébastien Gérard
CEA LIST, France

Aurora Ramírez
Univ. of Córdoba, Spain
@aurora_rqTwitter

PROGRAM COMMITTEE

• Syed Juned Ali (TU Wien, Austria)
• Shaukat Ali (Simula Research Laboratory and Oslo Metropolitan University, Norway)
• Wesley K. G. Assunção (Johannes Kepler University Linz, Austria)
• Robert Clarisó (Universitat Oberta de Catalunya, Spain)
• Istvan David (McMaster University, Canada)
• Davide Di Ruscio (Università degli Studi dell'Aquila, Italy)
• Martin Eisenberg (Johannes Kepler Universität Linz, Austria)
• Mattia Fumagalli (University of Bozen-Bolzano, Italy)
• Antonio Garmendia (Universidad Autónoma de Madrid, Spain)
• José Antonio Hernández López (Linköping University, Sweden)
• Rodi Jolak (Mid Sweden University, Sweden)
• Dimitris Kolovos (University of York, UK)
• Ana Cristina Marcén (Universidad San Jorge, Spain)
• Phuong T. Nguyen (University of L'Aquila, Italy)
• Bentley Oakes (Polytechnique Montreal, Canada)
• Iulian Ober (ISAE-SUPAERO, University of Toulouse, France)
• José Raúl Romero (University of Cordoba, Spain)
• Daniel Strüber (Chalmers | University of Gothenburg and Radboud University Nijmegen, Sweden and Netherlands)
• Gabriele Taentzer (Philipps-Universität Marburg, Germany)
• Massimo Tisi (IMT Atlantique, France)
• Marina Tropmann-Frick (Hamburg University of Applied Sciences, Germany)
• Steffen Zschaler (King's College London, UK)