MACHINE LEARNING-DRIVEN TESTING PROCESS OPTIMIZATION: A SYSTEMATIC REVIEW OF CROSS-INDUSTRY APPLICATIONS AND IMPLEMENTATION FRAMEWORKS

Abstract

This comprehensive article examines the integration of machine learning technologies in testing process optimization across diverse industries, addressing the growing need for efficient and adaptive testing methodologies. The article analyzes the implementation of predictive analytics and adaptive testing models in streamlining quality control workflows while maintaining rigorous testing standards. Through a systematic examination of current applications, this article explores how artificial intelligence-driven approaches can effectively reduce testing cycles, minimize bottlenecks, and enhance overall testing efficiency. The article synthesizes evidence from multiple sectors, including software development, manufacturing, and healthcare, to present a unified framework for machine learning integration in testing processes. Key considerations regarding infrastructure requirements, data quality, and organizational challenges are discussed, along with strategies for successful implementation. The findings demonstrate that machine learning applications in testing optimization offer significant potential for improving productivity while maintaining quality standards, though careful consideration must be given to implementation strategies and organizational readiness. This article contributes to the growing body of knowledge on intelligent testing systems and provides practical insights for organizations seeking to modernize their testing processes through machine learning integration.