Integrating Model-Driven Engineering with Machine Learning for Intelligent Systems: Literature Review

The rapid development of intelligent systems has created a need for techniques capable of handling complexities and developing in an automatic way. The integration of machine learning in model-driven engineering (MDE) offers several advantages for the development and improvement of complex and intelligent systems. While machine learning (ML) also offers robust techniques and MDE has systematic approaches aimed at code generation and abstraction, in this review, while presenting the principles of MDE and ML, the article also critically explores the integration of ML in MDE. Starting with the fundamental concepts of MDE, then the principles and algorithms of ML, the focus of the discussion is on how machine learning techniques can improve model-driven engineering processes. By presenting the motivations for their combined use in the development of intelligent systems, based on the recent literature, the article describes the challenges and potential future directions, noting that the integration of machine learning into model-driven engineering not only accelerates development but also enhances the adaptability and performance of intelligent and complex systems, making it an increasingly relevant approach to addressing the complexities of modern intelligent systems.