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On the assessment of generative AI in modeling tasks: an experience report with ChatGPT and UML

机译:关于生成式人工智能在建模任务中的评估——ChatGPT和UML的经验报告

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Most experts agree that large language models (LLMs), such as those used by Copilot and ChatGPT, are expected to revolutionize the way in which software is developed. Many papers are currently devoted to analyzing the potential advantages and limitations of these generative AI models for writing code. However, the analysis of the current state of LLMs with respect to software modeling has received little attention. In this paper, we investigate the current capabilities of ChatGPT to perform modeling tasks and to assist modelers, while also trying to identify its main shortcomings. Our findings show that, in contrast to code generation, the performance of the current version of ChatGPT for software modeling is limited, with various syntactic and semantic deficiencies, lack of consistency in responses and scalability issues. We also outline our views on how we perceive the role that LLMs can play in the software modeling discipline in the short term, and how the modeling community can help to improve the current capabilities of ChatGPT and the coming LLMs for software modeling.
机译:大多数专家都认为,大型语言模型 (LLM),例如 Copilot 和 ChatGPT 使用的模型,有望彻底改变软件开发的方式。目前,许多论文都致力于分析这些生成式人工智能模型在编写代码方面的潜在优势和局限性。然而,对LLM在软件建模方面的现状的分析很少受到关注。在本文中,我们研究了 ChatGPT 当前执行建模任务和协助建模者的能力,同时也试图找出其主要缺点。我们的研究结果表明,与代码生成相比,当前版本的 ChatGPT 在软件建模方面的性能有限,存在各种语法和语义缺陷、响应缺乏一致性和可扩展性问题。我们还概述了我们如何看待 LLM 在短期内在软件建模学科中可以发挥的作用,以及建模社区如何帮助提高 ChatGPT 和即将推出的 LLM 的当前软件建模能力。

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