An Intelligent Multi-Stage Framework For Web Application Testing: Risk-Based Compatibility Assessment And Reinforcement Learning-Driven Performance Optimization

Abstract

In modern web application development, ensuring cross-platform compatibility and optimal performance has become increasingly challenging due to the diversity of devices, browsers, and user loads. This paper proposes an intelligent two-level testing framework integrating machine learning and reinforcement learning to address these challenges. In the first level, a compatibility testing model utilizes historical defect data and platform-specific features to train a Random Forest classifier that predicts high-risk platform combinations, effectively reducing the total number of test cases by 40%. In the second level, a performance testing model employs Q-learning to dynamically explore and identify the system’s safe operational boundaries under varying load conditions. Experimental results demonstrate the effectiveness of the suggested strategy in enhancing flaw detection, optimising test resources, and self-learning system limitationswithout manual intervention. This hybrid intelligent framework significantly enhances the reliability, scalability, and cost-effectiveness of web application testing.