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All Journal Journal of Technology Informatics and Engineering
Ge Liu
Computer Science, USC, CA, USA
Computer Science & IT

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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 Vol. 5 No. 2 (2026): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i2.534

Abstract

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.
Cost-Aware LLM-Style Routing for AIOps Log Analysis: Log Parsing, Anomaly Detection, Fault Diagnosis, and Incident Summarization on LogEval Task Files Chenyu Li; Ge Liu; Zoe Zhao
Journal of Technology Informatics and Engineering Vol. 5 No. 2 (2026): AUGUST | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v5i2.538

Abstract

This study investigates a local and cost-aware routing framework for AIOps log analysis using the LogEval benchmark. The evaluation covers four tasks: log parsing, anomaly detection, fault diagnosis, and incident summarization. Instead of relying on external large language model APIs, the experiment implements deterministic local policies that simulate zero-shot and few-shot LLM-style inference under controlled token-cost and latency assumptions. Six approaches were compared: regex normalization, TF-IDF with machine learning, a local character-based classifier, zero-shot policy, few-shot retrieval policy, and a routing cascade. At a risk threshold of 0.20, the router directed only 12.9% of queries to the few-shot retrieval policy while achieving parsing accuracy of 0.991, anomaly F1-score of 1.000, diagnosis accuracy of 1.000, ROUGE-L of 0.743, and BLEU-1 of 0.814. The routing strategy reduced simulated token cost by 80.1% compared with always using few-shot retrieval. Additional unseen-template evaluation revealed limited generalization for closed-label classifiers and retrieval methods when encountering unseen patterns. The findings indicate that routing can effectively reduce AIOps inference costs, while further validation with real LLMs and stronger generalization testing are required before production deployment.
Co-Authors Binghua Zhou Boning Zhang Chenyu Li Xinzhuo Sun Zoe Zhao