Assessing Transfer in Ai Server Stress Testing And Ai Server Test Automation: Transparent Baselines and Failure Analysis

Abstract

A central challenge in AI server stress testing and AI server test automation is to compare studies whose mechanisms and validation settings do not share a single denominator. The present review uses automated stress testing designed around high-concurrency workloads and a process-level automation framework for testing large AI-server fleets as focal cases for a boundary-aware synthesis. A structured reading of two target studies and 12 verified companion references is conducted across five lenses: workload models, tail latency, resource contention, failure injection, capacity planning. Emphasis is placed on the provenance of evidence, the comparability of baselines, and the consequences of alternative explanations. The combined literature indicates that methodological gains become actionable only when workload models and tail latency are evaluated together and when limits associated with capacity planning are explicit. This shifts the emphasis from isolated scores toward traceable chains of evidence and decision relevance. The article concludes with a research agenda built around transparent comparators, targeted stress tests, and evidence records that can be reused without overstating causal or practical reach.