Which AI feature is commonly used for identifying defects in test automation?

Machine Learning is commonly used in test automation for defect identification. ML algorithms can analyze test results, learn from patterns, and detect anomalies that may indicate defects. This helps in improving the accuracy of defect identification and reduces the manual effort required for analyzing test outputs. Integrating machine learning into automation testing enhances the ability to identify subtle and complex defects in the application under test.