mlsanityData to 7 Oct 2026

From Code to Requirements: Agentic Reverse Engineering of Business Rules at Enterprise Scale

arXiv 2609.22719 ↗First posted 19 Sep 2026cs.SE cs.AIcited by 0

On the map: LLM agents and tool use → Software engineering coding agents

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use. Builds on 7 of its 22 references that are in the map. First posted on arXiv 19 Sep 2026.

Builds on

7 of 22 references.

Closest ancestors · 3

Same direction, newest first

  1. 2026AgentModernize: Preserving Business Logic in Legacy Modernization with Multi-Agent LLMs and Behavioral Specification Graphs17 May
  2. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering6 May
  3. 2023ChatDev: Communicative Agents for Software Development16 Jul

Other references · 4

  1. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,761
  2. 2023An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generationcited by 193
  3. 2023Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Surveycited by 92
  4. 2024Beyond-RAG: Question Identification and Answer Generation in Real-Time Conversationscited by 9

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Similar, not cited

Close in wording, no citation either way

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  2. 2026On the Viability of Requirements Generation From Code: An Experience Report ↗
  3. 2025The Software Engineering Simulations Lab: Agentic AI for RE Quality Simulations
  4. 2026Agent4RE: A Self-Refining Multi-agent Framework for End-to-End Software Requirements Engineering and Benchmarking
  5. 2026WiseSpec: Requirements-Driven Agents for Code Generation
  6. 2026Rethinking Software Engineering for Agentic AI Systems
  7. 2026TraceDev: A Traceability-Driven Multi-agent Framework for Requirement-to-Code Development
  8. 2026Cheap Code, Costly Judgment: A Case Study on Governable Agentic Software Engineering