AI and machine learning driven test automation: Revolutionizing software testing practices

Karnam, Vikram Sai Prasad (2025) AI and machine learning driven test automation: Revolutionizing software testing practices. World Journal of Advanced Engineering Technology and Sciences, 15 (2). pp. 1560-1571. ISSN 2582-8266

The integration of Artificial Intelligence and Machine Learning into software testing processes represents a transformative advancement in quality assurance practices. This technical article examines how AI-driven testing is revolutionizing traditional approaches through adaptive capabilities that respond dynamically to application changes. These intelligent systems introduce self-healing test scripts that automatically adapt to UI modifications, generate comprehensive test cases through sophisticated algorithms, and predict potential defects before they manifest in production environments. According to recent industry data, organizations implementing AI-based testing solutions have reported up to 40% reduction in testing cycles while improving defect detection rates by 35% on average. The economic benefits extend beyond immediate efficiency gains to strategic advantages in market responsiveness and customer satisfaction. Despite compelling advantages in resource optimization and defect detection, widespread adoption faces challenges including expertise shortages, substantial initial investments, technical integration complexities, and organizational resistance to changing established methodologies. Looking forward, emerging trends point toward increasingly autonomous testing capabilities, advanced natural language processing for test generation, sophisticated visual verification systems, and the progressive convergence of development and testing disciplines.