Abstract
The integration of Artificial Intelligence (AI) in software testing is revolutionizing the field by enhancing efficiency, accuracy, and coverage. This paper explores the impact of AI-driven techniques on software testing, contrasting them with traditional methods. AI advancements such as machine learning, deep learning, and predictive analytics facilitate automated decision-making, intelligent test case generation, and adaptive test execution, streamlining routine tasks and reducing human intervention. Despite these benefits, challenges such as data quality, algorithmic bias, and ethical concerns need to be addressed. The paper also discusses the potential for human-AI collaboration, leveraging the strengths of both to improve testing practices. By examining empirical evidence and case studies, this research aims to provide valuable insights for industry professionals, researchers, and stakeholders, highlighting future research avenues and opportunities in AI-driven software testing. --- This abstract synthesizes the key points from the provided document, presenting a concise overview of the study's objectives, findings, and implications.