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.

通过实验证明了利用大语言模型进行软件模型完善的潜力，尤其在处理概念上没有或只有少量、嘈杂的示例时具有广泛的推理能力。

利用大型语言模型进行软件模型补全：来自工业和公共数据集的结果

Leveraging Large Language Models for Software Model Completion: Results  from Industrial and Public Datasets

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.

利用大型语言模型进行软件模型演化的研究，探索了大型语言模型在软件工程中支持软件模型演化的潜力，通过使用模拟模型仓库进行的实验评估，发现大型语言模型在软件模型演化方面有很大的潜力，并值得进一步探索。