做遗留系统现代化或AI驱动代码迁移的团队，终于有了一个能自动生成可追溯规范的框架——Reversa 直接帮你把老代码变成AI智能体能理解的文档，建议做COBOL迁移或大型系统重构的开发者点开看看。
Reversa 是一个将遗留系统（如COBOL代码）转化为AI智能体可操作规范的框架。它通过多智能体流水线，自动映射项目表面、分析模块、提取隐含规则、合成架构并编写单元级规范，强调代码与规范的可追溯性、显式置信度标记和保留人工验证缺口。在将ATM系统从COBOL迁移到Go的案例中，生成了517条声明、10个缺口、53个Gherkin场景和9/11任务完成的重建计划。该框架以Node.js CLI形式发布，支持多种智能体引擎，并利用SHA-256清单保护修改文件。
Reversa: A Reverse Documentation Engineering Framework for Converting Legacy Software into Operational Specifications for AI Agents
Legacy systems concentrate business rules, architectural decisions, and operational exceptions that often remain implicit in code, data, configuration, and maintenance practices. At the same time, language-model-based coding agents depend on reliable context, correctness criteria, and behavioral contracts to modify real systems with lower risk. This paper presents Reversa, a reverse documentation engineering framework for converting legacy software into traceable operational specifications for AI agents. Reversa organizes this process as a multi-agent pipeline: specialized agents map the project surface, analyze modules, extract implicit rules, synthesize architecture, write unit-level specifications, and review generated claims. The proposal emphasizes three mechanisms: traceability between code and specification, explicit confidence marking, and preservation of gaps for human validation. The framework is distributed as a Node.js CLI, installs skills across multiple agent engines, and uses a SHA-256 manifest to preserve modified files during update or uninstall operations. In addition to the architectural description, we report an exploratory case study on migrating an ATM from COBOL to Go, in which the pipeline produced 517 claims classified by an internal confidence index, 10 registered gaps, 53 Gherkin parity scenarios, and a reconstruction plan with 9 of 11 tasks completed at inventory time. Final parity validation and cutover were not completed in this study. We do not claim broad empirical superiority; we position the contribution with respect to the literature on reverse engineering, LLM-based documentation, and software agents, and propose an evaluation protocol with metrics for coverage, traceability, confidence, utility, and cost.