Category: machine-learning, artificial-intelligence

As we’ve talked with some of the most innovative companies in the industry about their testing practices, we’ve noticed that they are concerned with both quality and speed. It makes it easier to ship code changes with confidence because your tests help ensure that the software continues to function as intended.

The problem with manual test selection is that it relies on static human understanding of the connections between software modules and tests.

Predictive test selection is a method of performing Test Impact Analysis that uses AI and machine learning to identify the right tests to run for a particular code change.

With predictive test selection, we’ve found that for many software projects you can run only 10–20% of the tests and to achieve 90% confidence that you’ve found a failure if one exists for a code change.

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