EVOLUTION OF SOFTWARE TESTING AND THE ROLE OF AI & ML

Abstract

The evolution of software testing has transitioned from manual execution to automation-centric frameworks and now toward intelligent, self-adaptive mechanisms driven by Artificial Intelligence (AI) and Machine Learning (ML). This paper proposes a lifecycle-based framework for integrating AI and ML techniques throughout the software testing continuum, aligning with modern development paradigms such as Agile and DevOps. By examining the progression from manual methods to predictive analytics and intelligent automation, we present a scholarly perspective on how AI-driven tools can improve test efficiency, scalability, and defect detection accuracy. Furthermore, this paper explores the role of AI/ML in autonomous test script generation, visual consistency validation, anomaly detection, and performance prediction. The proposed framework is reinforced with toolchain mapping, lifecycle integration strategies, and a real-world case study. This structured approach offers a replicable model for organizations aiming to enhance software quality assurance with intelligent, data-driven testing methodologies.