This study explores the capabilities of Large Language Models (LLMs), particularly OpenAI's ChatGPT, in addressing the challenges associated with software modeling, explicitly focusing on the bidirectional traceability problem between design models and code. The objective of this study is to demonstrate the proficiency of ChatGPT in understanding and integrating specific requirements into design models and code and its potential to offer solutions to the bidirectional traceability problem through a case study. The findings indicate that ChatGPT is capable of generating design models and code from natural language requirements, thereby bridging the gap between these requirements and software modeling. Despite its limitations in suggesting a specific method to resolve the problem using ChatGPT itself, it exhibited the capacity to provide corrections to be consistent between design models and code. As a result, the study concludes that achieving bidirectional traceability between design models and code is feasible using ChatGPT.

翻译：本研究探讨了大语言模型（LLMs），特别是OpenAI的ChatGPT，在应对软件建模相关挑战方面的能力，重点关注设计模型与代码之间的双向可追溯性问题。本研究的目的是通过案例研究，展示ChatGPT在理解和将特定需求整合到设计模型与代码中的熟练程度，以及其为双向可追溯性问题提供解决方案的潜力。研究结果表明，ChatGPT能够根据自然语言需求生成设计模型和代码，从而弥合这些需求与软件建模之间的鸿沟。尽管ChatGPT在提出利用自身解决该问题的具体方法方面存在局限性，但它表现出了提供修正以确保设计模型与代码之间一致性的能力。因此，研究得出结论，使用ChatGPT实现设计模型与代码之间的双向可追溯性是可行的。