How has AI evolved in software testing?

AI in software testing has advanced from simple automation to intelligent test generation, self-healing scripts, and predictive analytics, improving efficiency and accuracy in identifying defects.

What are the key AI-driven testing approaches?

AI-driven testing includes autonomous test generation, visual testing, predictive analytics, self-healing test scripts, and anomaly detection to optimize software quality.

What are the benefits of leveraging AI in software testing?

AI enhances test accuracy, reduces manual effort, accelerates test execution, improves defect detection, and optimizes test coverage, leading to higher software reliability.

What is the future of AI in software testing?

AI will drive continuous testing, autonomous test maintenance, intelligent defect prediction, and enhanced test coverage, making software quality assurance more efficient and adaptive.

What challenges exist in implementing AI in testing?

Challenges include high implementation costs, skill gaps, test data dependency, integration complexities, and the need for continuous model training for accurate testing results.

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