从代码到需求：企业规模下的业务规则智能体逆向工程
From Code to Requirements: Agentic Reverse Engineering of Business Rules at Enterprise Scale
- Cognizant（高知特）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一个七智能体协作框架，通过逆向工程企业软件自动生成业务需求文档，在九分钟内完成每项服务，成本降低超过98%。
AI中文摘要：
企业软件系统的业务需求很少以结构化形式被捕获；逻辑反而存在于源代码、配置文件和机构记忆中。当这些系统需要迁移、扩展或审计时，正式需求工件的缺失迫使团队进行昂贵且丢失知识的逆向工程。本文提出一个智能体框架，通过逆向工程未文档化的企业软件来自动生成业务需求文档（bRDs）。七个专门化的智能体协作发现用户旅程、提取业务规则，并从代码、测试套件、配置和可用文档中综合出锁定的bRD，由静态分析工具支持，由嵌入的专家从业者认知模型引导，并在受控升级点由人类审查者监督。在真实企业部署的实际执行轨迹上测量，该框架在每项服务九分钟内生成全面的bRDs，提取的规则与真实生产缺陷记录独立相互印证。在先前可比迁移到相同目标架构中，交付需要超过两年；在本项目中，该框架基于实际仓库指标使用IFPUG复杂度模型和行业基准劳动力费率得出的基线，实现了超过98%的成本降低。
英文摘要：
Business requirements for enterprise software systems are rarely captured in structured form; the logic resides instead in source code, configuration files, and institutional memory. When these systems must be migrated, extended, or audited, the absence of formal requirements artifacts forces teams into expensive, knowledge-losing manual reverse engineering. This paper presents an agentic framework that autonomously generates Business Requirements Documents (bRDs) through reverse engineering of undocumented enterprise software. Seven specialized agents collaborate to discover user journeys, extract business rules, and synthesize a locked bRD from code, test suites, configuration, and available documentation, supported by static analysis tools, guided by embedded expert practitioner cognitive models, and overseen by human reviewers at controlled escalation points. Measured on actual execution traces across a real enterprise deployment, the framework generates comprehensive bRDs in under nine minutes per service, with extracted rules independently corroborated against real production defect records. In a comparable prior migration to the same target architecture, delivery required over two years; on the present programme the framework achieves a cost reduction exceeding 98% against a baseline derived from actual repository metrics using IFPUG complexity models and industry benchmark labor rates.
