AI-Assisted Automated Testing Frameworks for Cross-Platform Android and iOS Mobile Applications: A Comprehensive State-ofthe- Art Review

Abstract

The rapid evolution of mobile operating systems—primarily Android and
iOS—has introduced unprecedented fragmentation in screen resolutions,
hardware architectures, system permissions, and rendering engines.
Delivering high-quality, seamless user experiences across these disparate
platforms presents a formidable challenge for software engineering teams.
Traditional automated test automation tools, such as Appium, Espresso, and
XCUITest, depend heavily on static locators and hand-crafted test scripts that
suffer from severe fragility, high maintenance overheads, and limited crossplatform script portability. In response, Artificial Intelligence (AI) and
Machine Learning (ML) techniques—including Computer Vision (CV),
Reinforcement Learning (RL), Natural Language Processing (NLP), and
Large Language Models (LLMs)—have been integrated into mobile
automation pipelines to enable dynamic GUI parsing, autonomous test case
generation, and real-time self-healing of test scripts. This paper provides a
rigorous, state-of-the-art review of AI-assisted automated testing frameworks
for cross-platform Android and iOS applications. We systematically analyze
current literature, core technological paradigms, research gaps, and
methodological approaches. Furthermore, we evaluate comparative
performance metrics, detailing how AI integration significantly reduces script
maintenance by over 70% and minimizes flaky tests. Finally, we address
current technical limitations, security concerns, and future research
trajectories in autonomous quality assurance.

KEYWORDS: Artificial Intelligence, Automated Mobile Testing, Cross-
Platform Automation, Android and iOS, Computer Vision, Self-Healing
Scripts, Reinforcement Learning, Large Language Models.