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Reversa:遺留系統轉AI規格

Computer Science > Software Engineering

arXiv:2605.18684 (cs)

[Submitted on 18 May 2026]

Title:Reversa: A Reverse Documentation Engineering Framework for Converting Legacy Software into Operational Specifications for AI Agents

Authors:Sanderson Oliveira de Macedo, Ronaldo Martins da Costa

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Abstract: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 this http URL 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.

Comments:

Preprint. Includes a generative AI use statement

Subjects:

Software Engineering (cs.SE); Artificial Intelligence (cs.AI)

Cite as:

arXiv:2605.18684 [cs.SE]

(or

arXiv:2605.18684v1 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2605.18684

Focus to learn more

arXiv-issued DOI via DataCite

Submission history From: Sanderson Macedo Doc [view email]

[v1]

Mon, 18 May 2026 17:23:13 UTC (1,130 KB)

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