Author: Sudhir Chaurasia
Abstract: Mobile applications are increasingly integral to everyday life, driving a demand for high-quality, reliable, and secure apps. Traditional manual testing methods are time-consuming, error-prone, and unable to meet the speed requirements of modern agile and continuous integration (CI) workflows. AI-driven automated testing frameworks have emerged as a powerful solution, leveraging machine learning, natural language processing, and computer vision to improve test coverage, reduce human intervention, and accelerate release cycles. This paper provides a comprehensive review of current AI-driven testing frameworks, exploring their architecture, methodologies, benefits, limitations, and future trends. The study highlights the applications of AI in mobile testing, including test generation, defect prediction, UI testing, and performance analysis. A comparative analysis of popular AI-based frameworks is presented alongside practical considerations for adoption. The paper concludes with insights into the evolving role of AI in shaping next-generation mobile testing practices.
Keywords: Mobile App Testing, Automated Testing, AI Testing Frameworks, Machine Learning, Mobile QA, Test Automation, Continuous Integration
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