AI and ML applied to QA automation

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in Quality Assurance (QA) automation is transforming software testing by enhancing accuracy, efficiency, and adaptability. AI-driven techniques, such as intelligent test case generation, self-healing automation scripts, and predictive analytics, enable faster defect detection and reduced maintenance efforts. ML models can analyze historical test data to optimize test execution, prioritize critical test scenarios, and identify patterns in software defects. Despite challenges such as data dependency, integration complexity, and the need for specialized expertise, AI and ML continue to drive innovation in QA automation. This paper explores the key applications, benefits, challenges, and future prospects of AI and ML in QA automation, highlighting their role in improving software reliability and development agility.