How is AI used in test automation?

AI makes test automation better by letting scripts fix themselves, creating smart test cases, finding anomalies, and doing predictive analytics. It makes tests more efficient and cuts down on the work needed to keep things running.

What are the key benefits of using AI/ML in test automation?

AI/ML reduces test maintenance, detects flaky tests, improves test accuracy, enables faster issue detection, and offers predictive analytics. These benefits lead to smarter automation, quicker releases, and enhanced decision-making throughout the software development lifecycle.

Is AI/ML test automation suitable for all types of projects?

AI/ML is ideal for complex, large-scale, and frequently changing projects. It may not offer significant ROI for small or static applications where traditional test automation already performs efficiently with minimal maintenance needs.

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