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

TestBrain connects to your code repository to analyze the commit history.

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%.

Import Automated
Test Results

TestBrain analyzes the test results in JUnit, XUnit, or NUnit xml format, identifies and quarantines flaky failures and failures due to open bugs. From the code failures, TestBrain continues to refine its risk model. The more tests and the more failures TestBrain sees, the more accurately it can determine which tests are needed to check new changes.

Organize Test Results

TestBrain organizes the results of automated testing, grouping together all failures caused by the same defect. TestBrain 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.

Speed Up Your QA Testing
with TestBrain

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