Adaptive Automation
Adaptive Automation is a software development and testing approach that dynamically adjusts automation strategies based on real-time feedback, application changes, and environmental conditions. It leverages AI, machine learning, and analytics to optimize test execution, maintenance, and coverage without constant manual intervention. This methodology aims to enhance efficiency, reduce flakiness, and improve the reliability of automated processes in agile and DevOps environments.
Developers should learn Adaptive Automation when working on complex, rapidly evolving applications where traditional static automation becomes costly and brittle, such as in continuous integration/continuous deployment (CI/CD) pipelines. It is particularly useful for reducing maintenance overhead in test suites, handling dynamic user interfaces, and scaling automation across diverse platforms and devices. By adopting this, teams can achieve faster feedback loops and higher test resilience in modern software development.