"Artificial intelligence and machine learning" in automated testing means:

1. _Predictive analytics_: AI and ML predict testing outcomes, identifying potential issues before they occur.

2. _Test case generation_: AI and ML generate test cases, reducing manual effort and increasing coverage.

3. _Test data generation_: AI and ML generate test data, ensuring relevant and realistic inputs.

4. _Intelligent test automation_: AI and ML optimize test automation, adapting to changing systems and conditions.

5. _Anomaly detection_: AI and ML detect anomalies in testing data, identifying potential issues.

6. _Root cause analysis_: AI and ML help identify root causes of issues, streamlining debugging.

7. _Continuous learning_: AI and ML continuously learn from testing data, improving testing effectiveness.

8. _Autonomous testing_: AI and ML enable autonomous testing, reducing human intervention and increasing efficiency.

By leveraging AI and ML, automated testing becomes more intelligent, efficient, and effective, ensuring higher quality and reliability in software development.