发表机构
University of Klagenfurt; Université de Kinshasa（克拉根福大学; 金沙萨大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
EA-Ops是一个Git原生架构即代码框架，通过YAML和ArchiMate 3.2验证实现持续企业架构治理，在50,000对象规模下达到100%准确率，并识别了扩展瓶颈。
AI 中文摘要
企业架构（EA）仓库经常将架构模型与用于变更软件和基础设施的工程工作流分离。本文介绍了EA-Ops，一个开源的Git原生企业架构即代码框架，它以YAML表示架构事实，根据ArchiMate 3.2配置文件验证类型化关系，强制执行组织特定的治理规则，执行基于图的影响分析，并从同一审查源发布面向人类的报告和静态交互门户。我们使用可复现的GitHub Actions测试工具评估EA-Ops。八种独立注入的结构性、语义性和治理性故障类别各执行了30次试验；所有240次试验均与真实情况完全匹配，精确率、召回率和F1值均为1.000。可扩展性实验进行了30次重复测量，达到50,000个对象和100,000个关系：中位验证时间为52.582秒，中位影响遍历时间为627.000毫秒，峰值常驻内存集大小为919.2 MB。一个包含十个场景的Metroville数字许可参考架构在每个场景中都产生了精确的验证结果，并与独立的广度优先搜索预言机达成了精确的影响集一致性。配置的100,000对象端到端基准测试成功生成了其模型，但在性能阶段超过了180分钟的CI预算；没有推断任何时间结果。在50,000个对象规模下，Markdown报告生成而非语义验证是主要的扩展瓶颈。结果支持Git原生持续治理作为数万对象规模下实用的EA运营模式，同时为更大的仓库定义了明确的限制和优化目标。
英文摘要
Enterprise architecture (EA) repositories frequently separate architecture models from the engineering workflow used to change software and infrastructure. This article presents EA-Ops, an open-source Git-native Enterprise Architecture-as-Code framework that represents architecture facts as YAML, validates typed relationships against an ArchiMate 3.2 profile, enforces organization-specific governance rules, performs graph-based change-impact analysis, and publishes human-facing reports and a static interactive portal from the same reviewed source. We evaluate EA-Ops with a reproducible GitHub Actions harness. Eight independently injected structural, semantic, and governance fault classes were executed across 30 trials each; all 240 trials matched ground truth exactly, with precision, recall, and $F_1$ of 1.000. Scalability experiments with 30 measured repetitions reached 50,000 objects and 100,000 relationships: median validation time was 52.582~s, median impact traversal was 627.000~ms, and peak resident-set size was 919.2~MB. A ten-scenario Metroville digital-permit reference architecture produced exact validation outcomes and exact impact-set agreement with an independent breadth-first-search oracle in every scenario. The configured 100,000-object end-to-end benchmark generated its model successfully but exceeded the 180-minute CI budget during the performance stage; no timing result is extrapolated. At 50,000 objects, Markdown report generation rather than semantic validation is the dominant scaling bottleneck. The results support Git-native continuous governance as a practical EA operating model at tens-of-thousands-of-object scale while defining clear limits and optimization targets for larger repositories.
