A large telecom service provider has been implementing DevOps practices to transition to an agile delivery lifecycle. Naturally, they were concerned about the volume of tests they needed to run in conjunction with frequent code changes and builds associated with their bi-weekly release cycles. They were typically required to run over 500 tests per build of an application (including unit, component, and BDD tests, API and integration tests, and system tests), the majority of which were manual. Carrying out all of these tests would significantly slow their release cycle. They were able to reduce the volume of tests by 70% to 90% depending on the application by implementing CDD and Test Advisor. By reducing the number of tests, they were able to significantly reduce elapsed time and testing-related delays.
We have only begun to scratch the surface of AI/ML applications in testing and quality – and a vast realm of possibilities awaits. Several key use cases that we are considering for future enhancements include defect prediction and source identification, quantification of release/deployment risk prior to going live, and automated test (and test data) generation based on production usage scenarios.