  Modeling structure and behavior of software systems plays a crucial role in
the industrial practice of software engineering. As with other software
engineering artifacts, software models are subject to evolution. Supporting
modelers in evolving software models with recommendations for model completions
is still an open problem, though. In this paper, we explore the potential of
large language models for this task. In particular, we propose an approach,
retrieval-augmented generation, leveraging large language models, model
histories, and retrieval-augmented generation for model completion. Through
experiments on three datasets, including an industrial application, one public
open-source community dataset, and one controlled collection of simulated model
repositories, we evaluate the potential of large language models for model
completion with retrieval-augmented generation. We found that large language
models are indeed a promising technology for supporting software model
evolution (62.30% semantically correct completions on real-world industrial
data and up to 86.19% type-correct completions). The general inference
capabilities of large language models are particularly useful when dealing with
concepts for which there are few, noisy, or no examples at all.
