This study explores the capabilities of Large Language Models, 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. We also explore 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（特别是OpenAI的ChatGPT）在应对软件建模相关挑战中的能力，重点关注设计模型与代码之间的双向可追溯性问题。本研究旨在证明ChatGPT在理解特定需求并将其整合到设计模型与代码中的熟练程度。我们还通过案例研究，探讨了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能够从自然语言需求生成设计模型与代码，从而弥合了需求与软件建模之间的鸿沟。尽管其在建议使用ChatGPT本身解决该问题的具体方法上存在局限性，但它展现出了提供修正以确保设计模型与代码一致性的能力。因此，研究得出以下结论：利用ChatGPT实现设计模型与代码间的双向可追溯性是可行的。