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Boning Zhang
Computer Science, Georgetown University, DC, 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.
Privacy-Robust Incrementality Estimation in Cookieless Settings via Uplift Modeling: Reproducible Evidence from the Hillstrom E-Mail Experiment Jingwen Bai; Haozhe Wang; Qiyou Wu; Boning Zhang
Journal of Technology Informatics and Engineering Vol. 5 No. 1 (2026): APRIL | 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.v5i1.468

Abstract

Measuring advertising incrementality in the absence of user-level identifiers is increasingly constrained by platform policies and privacy regulations. In cookieless environments, practitioners often observe only aggregated or weak signals (e.g., cohort-level conversion counts) and must still estimate the causal lift of an intervention while quantifying uncertainty. This paper studies cookieless incrementality evaluation through the lens of uplift and individual treatment effect (ITE) modeling under explicit privacy constraints. We conduct full experimental evaluations on the MineThatData (Hillstrom) E-Mail Analytics Challenge dataset (64,000 customers in a randomized controlled experiment with three arms). We cast the task as a binary treatment problem—sending any e-mail campaign versus sending none—and compare six ITE estimators (S-, T-, X-, R-, and doubly robust learners, plus transformed-outcome regression) against cohort-only estimators that emulate cookieless measurement. The cohort estimator uses only aggregated counts and a Bayesian beta–binomial model to shrink noisy rates, and we evaluate robustness under k-anonymity thresholds and Laplace-noised differentially private aggregates. Across held-out test data, the best ID-level model (T-learner with logistic regression) achieves a Qini coefficient of 6.675 and improves the estimated policy conversion rate when targeting the top 20% of customers by predicted uplift. Cohort-only estimation retains a weaker and more variable signal; its point estimate is sensitive to privacy constraints but yields valid uncertainty intervals with 0.892 empirical coverage for a 95% interval in cohort-level validation. The results demonstrate that (i) causal lift is estimable without identifiers when randomized experimentation is available, (ii) doubly robust estimators provide strong performance and fast scoring, and (iii) privacy-preserving aggregation introduces an accuracy–privacy trade-off that can be quantified and monitored using bootstrap and Bayesian uncertainty.
Co-Authors Binghua Zhou Ge Liu Haozhe Wang Jingwen Bai Qiyou Wu Xinzhuo Sun