  Modeling structure and behavior of software systems plays a crucial role, in
various areas of software engineering. As with other software engineering
artifacts, software models are subject to evolution. Supporting modelers in
evolving models by model completion facilities and providing high-level edit
operations such as frequently occurring editing patterns is still an open
problem. Recently, large language models (i.e., generative neural networks)
have garnered significant attention in various research areas, including
software engineering. In this paper, we explore the potential of large language
models in supporting the evolution of software models in software engineering.
We propose an approach that utilizes large language models for model completion
and discovering editing patterns in model histories of software systems.
Through controlled experiments using simulated model repositories, we conduct
an evaluation of the potential of large language models for these two tasks. We
have found that large language models are indeed a promising technology for
supporting software model evolution, and that it is worth investigating further
in the area of software model evolution.
