A Comprehensive Hybrid Grey Box Testing Framework for Scalable and Intelligent Mobile Application Quality Assurance

Mobile application ecosystems demand high reliability and responsiveness, particularly in mission-critical and user-sensitive contexts. Traditional testing methods fall short in meeting the complexity, diversity, and dynamism of modern Android applications. This paper proposes a comprehensive Hybrid Grey Box Testing (HGBT) framework that integrates static and dynamic analysis, AI-based decision-making, and modular orchestration to deliver robust quality assurance. Our enhanced architecture outperforms existing solutions by improving test coverage, fault detection, and automation compatibility. Extensive experimentation on benchmark apps demonstrates the practical advantages of this intelligent hybrid strategy. We clearly define our hypothesis and validate it with statistical analysis using simulated datasets.