Achieve Complete Automation with Artificial Intelligence and Machine Learning

As agile models become more prominent in software development, testing teams must shift from slow manual testing to automated validation models that accelerate the time to market. Currently, automation test suites are valid only for some releases, placing greater pressure on testing teams to revamp test suites, so they can keep pace with frequent change requests. To address these challenges, artificial intelligence and machine learning (AI/ML) are emerging as viable alternatives to traditional automation test suites. This paper examines the existing challenges of traditional testing automation. It also discusses five use-cases and solutions to explain how AI/ML can resolve these challenges while providing complete and intelligent automation with little or no human intervention, enabling testing teams to become truly agile.