ReqFusion: 一个多提供者框架用于跨软件领域的自动化 PEGS 分析
ReqFusion: A Multi-Provider Framework for Automated PEGS Analysis Across Software Domains
AI总结：
本文提出 ReqFusion 框架，利用多大型语言模型提供者自动提取和分析软件需求，通过 PEGS 方法提升准确性与效率，实验显示 PEGS 引导提示在 F1 分数上优于通用提示。
摘要：
需求工程是软件开发过程中至关重要的但劳动密集的阶段。本文介绍了 ReqFusion：一个增强人工智能的系统，利用多个大型语言模型（LLM）提供者自动提取、分类和分析软件需求。ReqFusion 架构整合了 OpenAI GPT、Anthropic Claude 和 Groq 模型，用于从各种文档格式（PDF、DOCX 和 PPTX）中提取功能性和非功能性需求，适用于学术、工业和招标提案场景。系统使用一种领域无关的提取方法，并按照 Bertrand Meyer 引入的项目、环境、目标和系统（PEGS）方法生成需求。主要思想是，由于 PEGS 格式详细，LLM 有更多关于需求的信息和提示，产生比简单通用请求更好的结果。消融研究证实了这一假设：PEGS 引导提示在相同多提供者配置下实现了 0.88 的 F1 分数，相比通用提示的 0.71。评估使用了 18 个真实世界文档生成 226 个需求，其中 54.9% 为功能性需求，45.1% 为非功能性需求，涵盖学术、商业和技术领域。对五个项目进行扩展评估，共计 1,050 个需求，显示提取准确性显著提高，分析时间比手动方法减少了 78%。多提供者架构通过模型共识和回退机制增强可靠性，而基于 PEGS 的方法确保覆盖所有需求类别。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
