Requirements engineering is a vital, yet labor-intensive, stage in the software development process. This article introduces ReqFusion: an AI-enhanced system that automates the extraction, classification, and analysis of software requirements utilizing multiple Large Language Model (LLM) providers. The architecture of ReqFusion integrates OpenAI GPT, Anthropic Claude, and Groq models to extract functional and non-functional requirements from various documentation formats (PDF, DOCX, and PPTX) in academic, industrial, and tender proposal contexts. The system uses a domain-independent extraction method and generates requirements following the Project, Environment, Goal, and System (PEGS) approach introduced by Bertrand Meyer. The main idea is that, because the PEGS format is detailed, LLMs have more information and cues about the requirements, producing better results than a simple generic request. An ablation study confirms this hypothesis: PEGS-guided prompting achieves an F1 score of 0.88, compared to 0.71 for generic prompting under the same multi-provider configuration. The evaluation used 18 real-world documents to generate 226 requirements through automated classification, with 54.9% functional and 45.1% nonfunctional across academic, business, and technical domains. An extended evaluation on five projects with 1,050 requirements demonstrated significant improvements in extraction accuracy and a 78% reduction in analysis time compared to manual methods. The multi-provider architecture enhances reliability through model consensus and fallback mechanisms, while the PEGS-based approach ensures comprehensive coverage of all requirement categories.

翻译：需求工程是软件开发过程中至关重要但劳动密集的阶段。本文介绍ReqFusion：一种AI增强系统，能够利用多个大语言模型（LLM）供应商自动完成软件需求的提取、分类和分析。ReqFusion的架构整合了OpenAI GPT、Anthropic Claude和Groq模型，可从学术、工业和招标提案场景中各种文档格式（PDF、DOCX和PPTX）中提取功能性和非功能性需求。该系统采用领域无关的提取方法，并遵循Bertrand Meyer提出的项目、环境、目标与系统（PEGS）方法生成需求。核心思想在于：由于PEGS格式具有详细性，LLM能够获取更多关于需求的信息和线索，从而比简单通用请求产生更优结果。一项消融研究验证了这一假设：在相同多供应商配置下，基于PEGS的提示方法F1得分为0.88，而通用提示方法仅为0.71。评估使用了18份真实文档，通过自动化分类生成226项需求，其中学术、商业和技术领域中功能性需求占比54.9%，非功能性需求占45.1%。对五个项目、1050项需求的扩展评估显示，相比手动方法，提取准确率显著提升，分析时间减少78%。多供应商架构通过模型共识和回退机制增强了可靠性，而基于PEGS的方法确保了所有需求类别的全面覆盖。