基于大语言模型的软件模型演化：在模拟、公开和工业数据集上的实验
Software Model Evolution with Large Language Models: Experiments on Simulated, Public, and Industrial Datasets
AI总结：
本文提出RAMC方法，利用大语言模型实现软件模型补全，通过实验验证其在工业和公开数据集上的有效性。
摘要：
软件系统结构和行为建模在软件工程工业实践中起着关键作用。与其他软件工程制品一样，软件模型也面临演化的问题。支持模型设计者在模型演化过程中提供模型补全建议仍然是一个开放性问题。本文探讨了大语言模型在该任务中的潜力。特别是，我们提出了一种方法RAMC，利用大语言模型、模型历史和检索增强生成技术进行模型补全。通过在三个数据集上的实验，包括一个工业应用、一个公开开源社区数据集和一个受控的模拟模型仓库数据集，我们评估了大语言模型在RAMC下的模型补全潜力。我们发现，大语言模型确实是一种支持软件模型演化有前途的技术（在真实世界工业数据上实现了62.30%的语义正确补全，最高可达86.19%的类型正确补全）。大语言模型的一般推理能力在处理缺乏、噪声或没有示例的概念时特别有用。
英文摘要：
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, RAMC, 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 RAMC. 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.
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