  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.
