Generative AI and large language models (LLMs), such as Copilot and ChatGPT can complete code based on initial fragments written by a developer. They are integrated in software development environments such as VS code. Many papers analyse the advantages and limitations of these approaches for code generation. Besides some deficiencies, the produced code is often correct and the results are improving.

However, a surprisingly small amount of work has been done in the context of software modeling. The paper from Cámara et al. concludes that while the performance of the current LLMs for software modeling is still limited (in contrast to code generation), there is a need that (in contrast to code generation) we should adapt our model-based engineering practices to these new assistants and integrate these into MBSE methods and tools.

The goal of this post-doc is to explore generative AI in the context of system modeling and associated tool support. For instance, AI assistance can support the completion, re-factoring and analysis (for instance identified design patterns or anti-patterns) at the model level. Propositions are discussed in the team and in a second step prototyped and evaluated the mechanism in the context of the open-source UML modeler Papyrus.