对比四款大模型生成餐厅管理系统功能需求，发现Claude最完整，Gemini最精准。
LLM assisted web application functional requirements generation: A case study of four popular LLMs over a Mess Management System
- 用零样本提示词让大模型生成用例、业务规则和协作流程
- Claude生成用例最全但有冗余，Gemini最精准，四模型均缺业务规则
- 适合想用大模型辅助写需求的开发者和产品经理参考
大语言模型（LLMs）已显著影响软件工程，助力开发人员在各阶段生成所需文档。本文通过案例研究，比较GPT、Claude、Gemini和DeepSeek在生成餐厅管理系统功能规格方面的能力，包括用例、业务规则和协作工作流。评估基于语法与语义正确性、一致性、无歧义性和完整性，对比原始参考规范。结果表明，四款模型均能生成语法和语义正确的非模糊用例与流程，但存在不一致问题，且生成完整性差异明显。Claude和Gemini均生成全部参考用例，其中Claude最完整但略冗余；工作流生成也呈现类似趋势。然而，所有模型在生成业务规则时表现不佳，DeepSeek生成最多参考规则但完整性不足。总体而言，Claude生成的规格更完整，Gemini则更精确。
原文摘要 · Abstract (English)
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.
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