Machine Learning to
Speed Up QA Testing
TestBrain’s machine learning speeds up automated testing by 10x, quarantines flaky failures, and prioritizes your manual testing where the defects are most likely.
Machine Learning Analyzes Your Repository
Machine Learning Determines Risks in Each Commit
TestBrain finds patterns in the commits which led to defects in the past and builds a model to determine the risk profile of each new code change. The machine learning examines the type of task, the scope of changes, size of each change, area of the code, and who created the defects. More details on TestBrain’s machine learning risk model can be found here.
Machine Learning for Test Orchestration Optimization
TestBrain integrates with your repository, CI/CD, and test automation framework. For each new commit, TestBrain’s machine learning determines which tests are needed to check the specific code changes, and triggers the CI to run the subset of tests.
Reduce Testing by 90%
By avoiding running the vast majority of tests that are not checking any code that has changed, TestBrain reduces the number of tests to run for each commit by 90% – 98%.