一句话：本地LLM提取四视图规约、云端模型仅据规约生成多目标代码，实现保留知识产权的企业系统现代化。
针对跨多站点、技术栈异构的港口终端操作系统现代化，云基LLM方案因知识产权与安全顾虑难以落地。作者提出混合流水线：本地LLM两遍提取结构域模型、行为规则、流程状态机与界面呈现四视图形式规约，覆盖48个限界上下文；云端模型仅据规约生成目标实现，不接触遗留代码。在CRM探针上，最佳本地模型结构保真度0.83，7个候选中2个无法输出可解析JSON；提示工程将域模型语义纯度从0.71提至0.94，遗留文档几乎无增益。按测试优先方法，仅从规约生成5451个行为测试，并产出Python/FastAPI与.NET 8 Blazor两个可运行应用栈。
We report on the automated modernization of a business-critical terminal operating system deployed across multiple sites of a major European port infrastructure company, comprising a heterogeneous technology stack accumulated over many years of continuous development: over 22,000 source files, about 10,000 of them C#, a multi-layered web forms UI, a complex Oracle database, and extensive operational documentation. Most current LLM-assisted modernization approaches rely on cloud-based models, raising intellectual property and security concerns that prevent adoption in industrial settings. We present a hybrid pipeline in which local LLMs extract a formal four-view specification that keeps proprietary source code entirely on-premises, while cloud models generate target implementations from specifications only, never accessing the legacy codebase. The pipeline extracts structural domain models, behavioral rules, process state machines, and screen presentations across 48 bounded contexts via a two-pass LLM extraction approach. Against a curated reference on a CRM probe, the best local model reaches a structural fidelity of 0.83, while two of seven candidates fail to emit parseable JSON at all; prompt engineering raises the semantic purity of the extracted domain model from 0.71 to 0.94, whereas legacy documentation adds almost nothing. Following a test-first approach, 5,451 behavioral tests were generated from specifications alone before any target implementation, and two runnable application stacks (Python/FastAPI and .NET 8 Blazor) were produced. We report concrete lessons on prompt engineering, structured output generation, and the practical boundaries between automation and manual intervention.
@inproceedings{paperbot12470,
title = {IP-Preserving Enterprise System Modernization via LLM-Driven Specification Extraction and Multi-target Code Generation},
author = {Andreas Biesdorf and Eckhard Seibert and Nicole Wengatz and Bart Carpels and Tom Schoonbaert},
booktitle = {ASE},
year = {2026},
doi = {10.1145/3832783.3834533}
}