MDE Intelligence

7th Workshop on Artificial Intelligence and Model-driven Engineering
Co-located with MODELS. October, 2025. Michigan, USA.

#mdeintelligenceX

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.

For this edition, the workshop has the special theme Enhancing MDE in the Era of Foundation Models and LLMs. We specifically invite papers that explore novel techniques of incorporating Foundation Models (FM) and Large Language Models (LLMs) in MDE.

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 foundation models (FM), large language models (LLMs), Generative AI and machine learning to modelling problems;
    • Enhanced prompt engineering techniques for generating reliable modeling artifacts
    • 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;
    • Reinforcement learning to optimize modelling tasks;
    • Generation of synthetic yet reliable modeling artifacts leveraging AI, ML, and foundation models;
    • Ethical aspects on the application of AI to modeling (responsibility, fairness, bias, etc.).

  • 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,
    • MDE techniques for prompt engineering.

  • General

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

SUBMISSIONS

Submissions must adhere to the IEEE formatting instructions, which can be found here We ask for two type of contributions:
  • 1) Research papers: 10 pages,
  • 2) Vision papers, experience papers or demos: 5 pages.
Submissions must be uploaded through EasyChair in the following link https://easychair.org/conferences/?conf=mdeintelligence2025.

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 joint workshop proceedings published by the ACM.

Papers submitted to MDE Intelligence must not be under review or submitted for review elsewhere whilst under consideration for MDE Intelligence. Contravention of this concurrent submission policy (as stated explicity by the IEEE on https://www.comsoc.org/publications/ieee-communications-society-policy-plagiarism-and-multiple-submissions) (as stated explicity by the ACM on https://www.acm.org/publications/policies/simultaneous-submissions) will be deemed as a serious breach of scientific ethics, and appropriate action will be taken in all such cases.

IMPORTANT DATES

  • Abstract submission: July 3, 2025
  • Paper submission: July 10, 2025 (UPDATED)
  • Notification: July 31, 2025
  • Camera-ready: August 7, 2025
  • Workshop: October 5-7, 2025


CALL FOR LIGHTNING TALKS

The 7th edition of the MDE Intelligence workshop will be co-located with MODELS 2025 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 30th, 2025.

PROGRAM

Full-Day Workshop Schedule (Michigan local time)

08:30 – 08:45 Opening
08:45 – 10:00 Session 1: Model Generation
Chair: Dominik Bork
  • [08:45 – 09:00] Fengjunjie Pan, Nenad Petrovic, Vahid Zolfaghari, Long Wen, and Alois Knoll. LLM-enabled Instance Model Generation
  • [09:00 – 09:15] Vittoriano Muttillo, Romina Eramo, Riccardo Rubei, and Luca Berardinelli. Coupling LLMs and Model-Driven Engineering to Support Synthetic Generation of BPMN Artifacts
  • [09:15 – 09:30] Giacomo Garaccione, Diego Maria Calabrese, Riccardo Coppola, and Luca Ardito. A comparison of different Large Language Models for the generation of UML class diagrams
  • [09:30 – 09:45] Lukas Netz, Finn Kampe, Jan Reimer, and Bernhard Rumpe. Unintended Changes: How LLMs Corrupt and Correct Textual Models
10:00 – 10:30 Break
10:30 – 11:05 Keynote
Speaker: Davide Di Ruscio
11:05 – 12:00 Session 2: Model Transformations and Reverse Engineering
Chair: Lola Burgueño
  • [11:05 – 11:20] Duy Dao, Alessio Bucaioni, and Antonio Cicchetti. Learning to Transform: Evaluating LLMs on Model Transformation by Example
  • [11:20 – 11:35] Sandra Greiner, Judi Abdullah, and Timo Kehrer. On the Generalization Capabilities of LLMs for Reverse Engineering Sequence Diagrams
  • [11:35 – 11:50] Ahmad Hatahet, Christoph Knieke, and Andreas Rausch. Generating Software Architecture Description from Source Code using Reverse Engineering and Large Language Model
12:00 – 13:30 Lunch Break
13:30 – 15:00 Session 3: Quality, Safety, and Evolution
Chair: Claudio Di Sipio
  • [13:30 – 13:45] Jan Gladiné, Bert Van Acker, and Joachim Denil. Towards Safety in Machine Learning Using Validity Frames
  • [13:45 – 14:00] Henrik Eckhardt and Jens Kosiol. Comparing High- and Low-Level Model Representations for Evolutionary Algorithms
  • [14:00 – 14:15] Tiago Sousa, Nicolas Guelfi, and Benoît Ries. Modeling AI-Driven Workflows for Ecosystem Resilience Prediction
14:15 – 15:00 Session 4: Lightning Talks, Discussion and Closing
Chairs: Lola Burgueño, Dominik Bork, and Claudio Di Sipio

Committees

ORGANIZING COMMITTEE

PROGRAM COMMITTEE

  • Syed Juned Ali (TU Wien, Austria)
  • Lissette Almonte (Universidad Autónoma de Madrid, Spain)
  • Wesley K. G. Assunção (North Carolina State University, United States)
  • Robert Clarisó (Universitat Oberta de Catalunya, Spain)
  • Davide Di Ruscio (Università degli Studi dell'Aquila, Italy)
  • José Antonio Hernández López (Universidad de Murcia, Spain)
  • Dimitris Kolovos (University of York, England)
  • Ana Cristina Marcén (Universidad San Jorge, Spain)
  • Vittoriano Muttillo (University of L'Aquila, Italy)
  • Lukas Netz (RWTH Aachen University, Germany)
  • Phuong T. Nguyen (University of L'Aquila, Italy)
  • James Pontes Miranda (CEA - List, France)
  • José Raúl Romero (University of Cordoba, Spain)
  • Daniel Strüber (Chalmers | University of Gothenburg, Radboud University Nijmegen, Sweden and The Netherlands)
  • Gabriele Taentzer (Philipps-Universität Marburg, Germany)
  • Massimo Tisi, (IMT Atlantique, France)
  • Marina Tropmann-Frick (Hamburg University of Applied Sciences, Germany)

Contact

If you have questions, contact us by email at: mdeintelligence2025@easychair.org