一句话：提出 RAMC，用大语言模型、模型历史与检索增强生成补全软件模型，支撑模型演化。
软件模型的结构与行为建模在工业软件工程中很重要，模型也需随需求演化，但为建模者推荐模型补全仍是开放问题。论文探索大语言模型在该任务上的潜力，提出 RAMC，结合大语言模型、模型历史和检索增强生成来生成补全。作者在工业应用、公共开源社区和受控模拟模型仓库三个数据集上评估。结果显示该方法有效：真实工业数据上 62.30% 的补全语义正确，类型正确补全最高达 86.19%；当概念缺少、噪声大或没有示例时，大语言模型的通用推理能力尤其有用。
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.
DOI 原文 ·
@inproceedings{paperbot8914,
title = {Software Model Evolution with Large Language Models: Experiments on Simulated, Public, and Industrial Datasets},
author = {Christof Tinnes and Alisa Welter and Sven Apel},
booktitle = {ICSE},
year = {2025},
doi = {10.1109/icse55347.2025.00112}
}