LLM-Style DevOps Copilot for Cloud-Native Troubleshooting: Retrieval-Augmented Runbook Generation and Command-Safety Evaluation - Boning Zhang; Xinzhuo Sun; Ge Liu; Binghua Zhou (Journal of Technology Informatics and Engineering, 2026)
Cloud-native incident response requires engineers to connect symptoms, observability signals, infrastructure state, and safe remediation commands under time pressure. Large language models can draft runbooks, but an ungrounded assistant can invent commands, recommend the wrong diagnostic path, or reproduce destructive operational shortcuts. This paper evaluates an LLM-style, retrieval-augmented DevOps Copilot simulation for cloud-native troubleshooting on the canonical Szaid3680/Devops Arrow export. The experiment indexes all 42,819 rows with the public Response, Instruction, and Prompt schema and evaluates a deterministic 400-query subset with TF-IDF, BM25, a compact dense-semantic baseline, RAG-style answer construction, reranking, command-safety checking, and the combined reranker-plus-checker pipeline. No live LLM inference is used in the executed experiment; the generation and checking components are deterministic so that the safety effects can be reproduced exactly. Results show that retrieval improves answer grounding but does not by itself guarantee safe automation: RAG-only reaches 0.2966 semantic similarity and emits matched unsafe command text at a rate of 0.0324. The command-safety checker reduces the matched unsafe command rate to 0.0000 for the declared rule set and keeps command validity at 0.9922. The full pipeline obtains 0.3051 semantic similarity, 0.4225 root-cause accuracy, 0.5825 root-category accuracy, and 0.0078 hallucinated-command rate. The findings support treating DevOps copilots as retrieval-grounded and policy-checked workflow systems rather than free-form chat agents.