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
- Linked via arxiv authorBonan Shen →
LLM-Driven CI-CD Workflow Intelligence for Cyber Systems Engineering
- Linked via arxiv authorJiazhou Gao →
LLM-Driven CI-CD Workflow Intelligence for Cyber Systems Engineering
- Linked via arxiv authorTao Ning →
LLM-Driven CI-CD Workflow Intelligence for Cyber Systems Engineering
- Linked via arxiv authorWei-Jung Huang →
LLM-Driven CI-CD Workflow Intelligence for Cyber Systems Engineering
- Linked via arxiv authorShuxin Liu →
LLM-Driven CI-CD Workflow Intelligence for Cyber Systems Engineering