AI-Driven Software Testing: Revolutionizing Test Automation and Quality Assurance

Abstract

Artificial Intelligence (AI) is transforming software testing by enabling intelligent automation, anomaly detection, and predictive analytics. This paper explores the impact of AI-driven software testing on quality assurance (QA) processes, focusing on AI-powered tools that enhance test coverage, defect detection, and test case generation. The study highlights key AI techniques such as machine learning, natural language processing (NLP), and reinforcement learning used in test automation. Case studies of organizations adopting AI-driven testing demonstrate improved defect identification, reduced testing time, and enhanced software reliability. The research also addresses challenges related to AI model training, data quality, and explains ability in test outcomes. Future directions involve the integration of AI with continuous testing pipelines and enhancing AI interpretability for better decision-making in QA.

Keywords: AI-Driven Testing, Test Automation, Machine Learning, Quality Assurance, Predictive Analytics.