Software Testing in the Era of AI: Leveraging Machine Learning and Automation for Efficient Quality Assurance

Abstract

Automation and machine learning incorporated into software testing procedures are significant improvements over current quality assurance procedures. The potential of AI-driven testing methodologies to improve software testing's efficacy and efficiency is examined in this paper. The study's principal goals are investigating AI-driven testing methods, empirical assessments, case studies, identification of issues and policy consequences, and recommendations for responsible adoption. A thorough analysis of the body of research on AI-driven testing, including case studies, research papers, and policy documents, is part of the process. The main conclusions highlight the efficiency gains made possible by intelligent test prioritizing, automated test generation, and anomaly detection. They also discuss the difficulties and policy ramifications of bias, data security, privacy, and regulatory compliance. The creation of moral standards, legal frameworks, and educational initiatives to encourage the appropriate and ethical application of AI-driven testing methodologies are examples of policy ramifications. This study advances knowledge about AI-driven testing and offers guidance to researchers, practitioners, and legislators involved in software quality assurance.