Impact-based test selection and prioritization is an intelligent solution that learns and develops a source code to test mapping. This method uses profilers and applies heuristics from historical data to generate the test suite based on the commit data that goes into every build.

It primarily consists of four main steps:

Source code to test case mapping. The tool uses a software agent during test case or test script execution. The agent attempts to map the test cases to the source code files, using profilers to instrument the byte code of the application and store it in a data lake.
Test prioritization heuristics. From historical test execution records and defects records, certain test prioritization heuristics are applied to determine the higher-priority test cases. This is an emerging field of data science known as MSR — mining software repositories.
Derive commit changes. After the build is generated, the agent creates a list of all added/modified/deleted files and pushes it into the test suite generator.
Test suite generator. Using the generated change set, the tool attempts to determine the correct impact-based suite based on code churn and a prioritized list of test cases using historical data.

These test-prioritization heuristics can further be refined to include certain reinforced learning (RL) algorithms to help refine the prioritized list of test cases. This is done by selecting and prioritizing test cases according to their duration, previous last execution, and failure history under the guidance of a reward function built into the algorithm.

Key benefits of impact-based test selection and prioritization

The benefits of this process are straightforward: It helps test teams select the optimum regression test suite based on code changes. This, in turn, improves product quality and streamlines the customer experience. The data-driven approach to impact analysis makes it immune to human error, leading to significant savings in time to determine the test regression suite. This is crucial for squads churning out frequent builds to test.

Test lifecycle automation is positioned to be one of the main disruptive innovations changing the way testing is performed in the coming decade. The use of AI/ML techniques will be a catalyst and primary growth driver in the software testing realm.

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