How can AI support Continuous Testing?

Artificial Intelligence (AI) simplifies continuous testing through automated test case generation from user requirements, code changes, and historical data. This makes continuous testing adaptable as the application is updated, without any manual effort.

Self-healing capabilities enable test scripts to be updated automatically due to UI or functionality changes saving a tester time on maintenance and stabilizing flaky tests that do not always behave consistently. This automation improves the speed of execution, parallel testing, and integration into CI/CD for quick feedback during release cycle iterations to have fast releases with consistent results.

AI-based defect prediction utilizes machine learning to understand historical bug trends, code complexity, and historical test data to target segments of the application that are at the greatest risk of failure. It also alerts the teams with regard to changes and historical bugs to prioritize tests based on risk and changes and to ensure the necessary coverage where tests are needed while maximizing the effective use of limited testing resources and avoiding extensive effort.