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.

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

• 1) Research papers: 10 pages,
• 2) Vision papers, experience papers or demos: 5 pages.

IMPORTANT DATES

• Paper submission: July 3, 2025
• Notification: July 31, 2025
• Camera-ready: August 7, 2025
• Workshop: October 5-7, 2025

Committees

ORGANIZING COMMITTEE

Lola Burgueño
Univ. of Malaga, Spain
@LolaBurguenoTwitter

Dominik Bork
TU Wien, Austria
@BorkDominikTwitter

Claudio Di Sipio
University of L'Aquila, Italy
@DsiClaudioTwitter

PROGRAM COMMITTEE