LLM-Driven CI-CD Workflow Intelligence for Cyber Systems Engineering
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-
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 56%Pipelex/pipelex →
- PossiblePossibly related (embedding) · 54%A system-level approach to prompt injection: separating instruction and data channels in LLM agents [P] →
- PossiblePossibly related (embedding) · 53%IEEE Rolls Out Large Language Models Virtual Training Course →
- PossiblePossibly related (embedding) · 53%huhusmang/Awesome-LLMs-for-Vulnerability-Detection →
- PossiblePossibly related (embedding) · 53%langgenius/dify →
- PossiblePossibly related (embedding) · 46%Improving my ci cd flow →
- PossiblePossibly related (embedding) · 46%NetAudit – CLI tool for network audits that plays nicely with scripts, CI/CD, and monitoring →
- LinkedLinked via arxiv author · 85%Bonan Shen →
“LLM-Driven CI-CD Workflow Intelligence for Cyber Systems Engineering”