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AI-Driven and Autonomous Testing

1st Elavarasi Kesavan, Full Stack QA Architect, Cognizant, elavarasikmk@gmail.com

ORCID https://orcid.org/0009-0008-3844-0286

Abstract
The rapid advancements in artificial intelligence (AI) have brought transformative changes to various sectors, culminating in innovative approaches to software testing (Shuroug A Alowais et al.; Nazir A et al., p. 111661-111661). The escalating complexity of software systems necessitates efficient testing methods, propelling the adoption of AI-driven and autonomous testing paradigms (Hassija V et al., p. 45-74; Armstrong M et al., p. 1001-1029). This paper provides a comprehensive systematic review of AI's integration into testing processes, employing rigorous methodology to examine current literature and industry practices. Through analysis of 50+ peer-reviewed sources and quantitative case studies from industry implementations, we demonstrate that AI can potentially reduce testing time by up to 50% while simultaneously increasing defect detection rates by 35% (Allioui H et al., p. 8015-8015; Chan CKY). The study introduces a structured implementation framework for organizations adopting AI-driven testing and presents a comparative analysis of machine learning techniques in testing applications (Ali S et al., p. 101805-101805; Yogesh K Dwivedi et al., p. 102642-102642). Key findings indicate that organizations implementing AI-driven testing report 40% reduction in time-to-market and 30% decrease in critical bugs reaching production (Karalis V, p. 14-44; Saeed W et al., p. 110273-110273). However, challenges related to data bias, model interpretability, and workforce adaptation remain significant (Elahi M et al.; Singh BJ et al., p. 119230-119230; Luo Y et al., p. 5211-5295). This research contributes to the field by identifying specific research gaps and providing evidence-based recommendations for practitioners transitioning from traditional to autonomous testing methodologies