面向网络系统工程的由大语言模型驱动的持续集成与持续交付工作流智能
LLM-Driven CI-CD Workflow Intelligence for Cyber Systems Engineering
AI总结：
研究面向网络系统工程的CI/CD工作流智能，通过基于大语言模型的分析管道，结合多种方法处理大量GitHub库数据，发现问题并得出结果，强调CI/CD可观测性应结合诊断、上下文和人工审查。
摘要：
CI/CD工作流已成为可执行的操作策略，是网络系统工程的重要测量点。近期大语言模型工作表明可从配置文件识别工作流阶段，但仅阶段标签无法判断工作流是否脆弱等。本文提出基于大语言模型的CI/CD分析管道，处理大量GitHub库数据，得出相关结果，支持结合诊断等的CI/CD可观测性。
英文摘要：
CI/CD workflows have become executable operational policy: they decide what gets built, tested, released, and deployed, and they mediate how maintainers interact with delivery infrastructure. That makes them an important measurement point for cyber-systems engineering. Recent large language model (LLM) work shows that workflow stages can be recognized directly from configuration files, but stage labels alone do not tell us whether a workflow is brittle, unusual for its ecosystem, or worth revising first. We present an LLM-based CI/CD analysis pipeline that combines repository enrichment, anti-pattern detection, stage mining, and recommendation generation over a large GitHub corpus. Starting from 59,550 repositories with at least 1,000 stars, we identify 34,225 projects with CI/CD and collect 127,559 configuration files. Across 75,201 analyzed workflows, the anti-pattern detector reports 434,769 findings, dominated by reliability and maintainability issues. Across 59,906 configurations, stage usage differs significantly by language ($χ^2 = 4168.88$, $p < 0.001$, Cramer's $V = 0.063$), and domain analysis shows distinct operational profiles, including higher release and cache usage in mobile projects. For repository-level recommendation generation, few-shot prompting performs best overall, averaging 8.25 recommendations per repository with 96.1% YAML-valid snippets. Taken together, the results argue for CI/CD observability that combines diagnosis, context, and human review rather than treating workflow mining as a stage-classification problem alone.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
