A Self-Adaptive, AI-Powered Testing Framework for Cross-Platform Systems: An Architectural Design and Industrial Evaluation

Abstract

The escalating complexity of cross-platform applications, which must operate seamlessly across a heterogeneous ecosystem of devices and operating systems, has rendered conventional script-based test automation unsustainable. These legacy approaches are plagued by inherent brittleness, inadequate test coverage, and exorbitant maintenance overhead, creating a critical bottleneck in modern DevOps pipelines. This paper proposes a novel, integrated framework for an intelligent testing ecosystem that leverages machine learning to engender self-adaptation, predictive analytics, and autonomous operation. We delineate a modular architecture incorporating three core intelligent capabilities: cognitive test generation using reinforcement learning and natural language processing,  self-healing test execution via multi-modal locator strategies and computer vision, and  predictive defect localization through ensemble-based risk modeling. The framework's efficacy is empirically validated through two longitudinal industrial case studies in the FinTech and E-commerce domains. Quantitative results demonstrate a 55-70% reduction in test maintenance effort, a 40% improvement in test coverage, and a 60-62.5% acceleration in regression testing cycles. Furthermore, we critically discuss implementation challenges—including data dependency, computational overhead, and model explainability—and propose a research trajectory toward causal inference and end-to-end autonomous testing systems. Our findings substantiate that the integration of AI is not merely an incremental enhancement but a paradigmatic shift essential for achieving robust, continuous quality assurance in cross-platform development.