AI-Driven Test Automation for Apple Device Network Performance Validation
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V7I2P112Keywords:
AI-Driven Testing, IOS, Apple Devices, Network Framework, XC Test Performance Tests, Anomaly Detection, Reinforcement Learning, Network Performance Validation, CI/CDAbstract
Apple devices operate across heterogeneous networks (Wi‑Fi, cellular, captive portals, VPNs, IPv4/IPv6), where performance regressions can emerge from OS updates, modem/firmware changes, access point configuration drift, or transport-layer evolution. Traditional scripted performance testing is brittle and often fails to distinguish true regressions from natural variance. This paper presents an AI-driven test automation framework for validating Apple device network performance at scale. The framework combines (i) automated scenario generation using reinforcement learning (RL) and constraints, (ii) device-side performance instrumentation using XCTest performance measurement APIs [1]–[3], (iii) network path awareness using Apple’s Network framework (e.g., NWPathMonitor, NWPath) [4], [5] and connection establishment reporting/metrics [6], [7], and (iv) anomaly detection over multi-dimensional telemetry informed by established anomaly detection literature [8]. Two case studies demonstrate tail-latency regression detection in connection establishment and resilience validation across Wi‑Fi↔cellular transitions. The approach integrates with CI/CD progressive delivery practices including automated tests, scanning, canary deployments, and rollback logic [9].
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References
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