James Pontes Miranda thesis defense

Model-driven engineering (MDE) promotes models as a key element in addressing the increasing complexity of the software systems’ lifecycle. Engineering systems with MDE involves various models representing different system aspects. This heterogeneity requires model federation capabilities to integrate viewpoints specific to multiple domains. Model View solutions address this challenge but still lack more automation support. This thesis explores the integration of Machine Learning (ML), notably Graph Neural Networks (GNNs) and Large Language Models (LLMs), in order to improve the definition and building of such views. The proposed solution introduces a twofold approach within the EMF Views technical solution. This allowed to partially automate the definition of model views at design time, and to dynamically compute intermodel links at runtime. Our results indicate that the application of Deep Learning (DL) techniques, in this particular MDE context, already allows to achieve a first relevant level of automation. More globally, this research effort contributes to the ongoing development of more intelligent MDE solutions