How does machine learning improve the process of test case prioritization in automation testing?
- Analyzing historical test execution data
- Generating random test case sequences
- Prioritizing test cases based on alphabetical order
- Running test cases in parallel
Machine learning in test case prioritization involves analyzing historical test execution data to identify patterns and prioritize test cases based on their historical significance. By understanding which test cases are more likely to find defects, machine learning helps optimize the testing process by focusing on critical areas, improving efficiency, and providing faster feedback on software quality.
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