Advancing Software Testing: Integrating AI, Machine Learning, and Emerging Technologies

Abstract

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into software testing has introduced transformative methodologies, enhancing efficiency, accuracy, and scalability. This paper explores contemporary advancements in AI-driven software testing, highlighting frameworks, tools, and techniques that optimize the testing lifecycle. By analyzing innovations such as AI-enhanced test automation, predictive analytics for defect detection, and large language models (LLMs) for test generation, we present a comprehensive overview of the current landscape. We also discuss challenges and opportunities in adopting these technologies, with an emphasis on ensuring quality in generative AI systems. The findings are substantiated with insights from recent studies and practical implementations, offering a roadmap for future research.