Machine Learning for
Faster, Smarter QA
TestBrain’s advanced machine learning analyzes your commit history to help you run the tests that are needed for the specific code changes, speeding up your automated testing by 10x and prioritizing your manual testing where it matters.
Machine Learning to Analyze Your Repository
TestBrain connects to any Git-based repository to analyze the commit history.
Machine Learning Determines Risks in Each Commit
TestBrain looks for patterns in the commits which led to defects and uses these patterns to determine the risk profile of new commits. The machine learning examines the type of task, the scope of changes, size of each change, area of the code where defects were created, and who created the defects.
Machine Learning for Test Orchestration Optimization
TestBrain integrates with your repository, CI/CD, and test automation framework. The repository notifies TestBrain of each new commit via a webhook. TestBrain’s machine learning then determines which tests are needed to check the specific code changes, and triggers the CI to run the tests.
Reduce Testing by 90%
By avoiding running the many tests that do not check anything that changed in the commit, TestBrain can reduce the number of tests that need to be run by 90%.
Appsurify reads the results from the test automation platform in JUnit, XUnit, or NUnit XML format. This allows Appsurify to organize test results and automate defect tracking while continuing to refine the risk assessment. The more tests and the more failures Appsurify sees, the more accurately it can determine which tests are needed for a particular commit.
Organize Test Results
Appsurify organizes the results of automated testing, grouping together all failures caused by the same defect. Appsurify also isolates failures due to flaky tests or unreliable automation from failures caused by code defects. And it automates the defect lifecycle, opening, tracking, and closing bugs for you.