Abstract

Software testing is critical for determining quality, reliability, and performance in contemporary software systems. Conventional automation methods tend to offer poor test coverage because of static execution patterns and pre-defined test cases. This work suggests a data-driven test automation framework that enhances adaptive test prioritization and execution strategies to maximize test coverage and defect detection. The model combines historical defect analysis, machine learning-based test selection, and real-time feedback mechanisms to dynamically adapt test cases according to software changes. Theoretical viability and comparative study suggest that the AI-based method significantly enhances test efficiency, scalability, and flexibility over conventional approaches. Anticipated outcomes include quicker execution times, wider test coverage, and less manual intervention, which positions it well for agile and CI/CD environments. This research gives us an idea of intelligent test automation’s future, with a scalable approach to dynamic software testing issues.