This research investigates the influence of the Gunning Fog Index (GFI) on the production of PlantUML use case diagrams by ChatGPT, within the context of software development projects, particularly focusing on E-commerce for Special Shoes.
The study emphasizes the impact of linguistic complexity, as measured by GFI, on the automated code-generation process. It reveals that lower GFI scores, indicative of clearer text, lead to more easily comprehensible code representations, while higher scores correlate with intricate but potentially less accessible code artifacts. This reflects a nuanced balance between detail and clarity in the specifications. A key finding is the significant contribution of integrating linguistic clarity met-rics like GFI into the code generation workflow. This integration is shown to enhance code quality, usability, and facilitate collaboration among stakeholders. Despite the study’s focus on GFI to the exclusion of other readability metrics, it provides valuable insights into the role of linguistic complexity in shaping software artifacts generated by ChatGPT. Recognizing the importance of readability allows developers to more effectively translate natural language specifications into machine-generated code, yielding benefits in diverse project scenarios.