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This study explores the capabilities of Large Language Models, particularlyOpenAI's ChatGPT, in addressing the challenges associated with softwaremodeling, explicitly focusing on the bidirectional traceability problem betweendesign models and code. The objective of this study is to demonstrate theproficiency of ChatGPT in understanding and integrating specific requirementsinto design models and code. We also explore its potential to offer solutionsto the bidirectional traceability problem through a case study. The findingsindicate that ChatGPT is capable of generating design models and code fromnatural language requirements, thereby bridging the gap between theserequirements and software modeling. Despite its limitations in suggesting aspecific method to resolve the problem using ChatGPT itself, it exhibited thecapacity to provide corrections to be consistent between design models andcode. As a result, the study concludes that achieving bidirectionaltraceability between design models and code is feasible using ChatGPT.

Exploring the ChatGPT Approach for Bidirectional Traceability Problem between Design Models and Code