LLM assisted web application functional requirements generation: A case study of four popular LLMs over a Mess Management System

--Like any other discipline, Large Language Models (LLMs) have significantly impacted software engineering by helping developers generate the required artifacts across various phases of software development. This paper presents a case study comparing the performance of popular LLMs--GPT, Claude, Gemini, and DeepSeek -- in generating functional specifications that include use cases, business rules, and collaborative workflows for a web application, the Mess Management System. The study evaluated the quality of LLM-generated use cases, business rules, and collaborative workflows in terms of their syntactic and semantic correctness, consistency, non-ambiguity, and completeness compared to the reference specifications against the zero-shot prompted problem statement. Our results suggested that all four LLMs can specify syntactically and semantically correct, mostly non-ambiguous artifacts. Still, they may be inconsistent at times and may differ significantly in the completeness of the generated specification. Claude and Gemini generated all the reference use cases, with Claude achieving the most complete but somewhat redundant use case specifications. Similar results were obtained for specifying workflows. However, all four LLMs struggled to generate relevant Business Rules, with DeepSeek generating the most reference rules but with less completeness. Overall, Claude generated more complete specification artifacts, while Gemini was more precise in the specifications it generated. Formally specifying software has remained one of the challenging tasks in software engineering. The challenges are apparent: the specifications must be well-defined, unambiguous, complete, consistent, and aligned with the stakeholder needs.