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.

翻译：对软件系统的结构与行为进行建模，在软件工程的多个领域发挥着关键作用。与其他软件工程制品类似，软件模型也会经历演进过程。如何通过模型补全设施为建模者提供演进支持，并实现诸如高频编辑模式等高层编辑操作，仍是一个待解决难题。近年来，大语言模型（即生成式神经网络）在包括软件工程在内的多个研究领域引起了广泛关注。本文探索了大语言模型在支持软件工程中软件模型演进方面的潜力。我们提出了一种方法，利用大语言模型实现模型补全，并发现软件系统模型历史中的编辑模式。通过使用模拟模型库进行的受控实验，我们评估了大语言模型在这两项任务中的潜力。研究发现，大语言模型确实是支持软件模型演进的一项有前景的技术，值得在软件模型演进领域进一步深入研究。