Automated decision-making ensures that the decisions are consistently made based on predefined rules and criteria, which can improve the overall quality of the testing process. This process has a three-pronged approach: predictive analysis, optimized test coverage, and self-healing.
Predictive analytics employs statistical techniques and machine learning algorithms to analyze historical data and predict the performance or behavior of the software under test. Optimized test coverage uses a combination of the latest techniques and tools that support test case selection, prioritization, and generation. The final facet is self-healing, which refers to the ability of a system automation suite to automatically detect and repair defects or failures without the need for human intervention. However, testing and validating the self-healing mechanisms is essential to ensure they function correctly, and that new defects or failures don’t appear.