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AI-driven streamlined modeling: experiences and lessons learned from multiple domains.

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Integrating artificial intelligence (AI) with model-driven engineering (MDE) can enhance its industrial adoption. Five case studies demonstrate how AI techniques can improve MDE activities, offering valuable insights for researchers and practitioners.

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Area of Science:

  • Software Engineering
  • Artificial Intelligence
  • Model-Driven Engineering

Background:

  • Model-driven engineering (MDE) offers benefits like abstraction and automation but faces limited industrial adoption.
  • Current integration of AI in MDE is nascent and requires a new perspective.

Purpose of the Study:

  • To explore the integration of AI techniques into model-driven engineering activities.
  • To provide a new perspective on leveraging AI to enhance MDE adoption and effectiveness.
  • To share practical experiences and lessons learned from industrial case studies.

Main Methods:

  • Conducted five industrial case studies applying AI techniques to various MDE activities.
  • Evolved existing MDE solutions with AI and incorporated AI from the outset of new solutions.
  • Documented experiences, challenges, and successful integration strategies.

Main Results:

  • Demonstrated that AI can significantly enhance the benefits of MDE, potentially outweighing costs.
  • Showcased diverse applications of AI across different MDE tasks.
  • Identified key lessons learned for successful AI integration in MDE.

Conclusions:

  • AI integration offers a promising path to increase the industrial relevance and adoption of MDE.
  • The presented case studies provide practical guidance for researchers and practitioners.
  • A shift in perspective is crucial for effectively harnessing AI in MDE.