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How Can AI and Machine Learning Transform QA Engineering?

AI-powered test generation accelerates coverage by analyzing requirements to propose test scenarios. Predictive analytics identifies high-risk modules using historical defect data, optimizing resource allocation. Self-healing test scripts leverage ML to detect UI changes and adapt automation flows, reducing maintenance. NLP-driven test specification allows teams to convert plain language requirements into executable tests, bridging communication gaps. ML-enhanced visual testing compares application states at the pixel level, isolating genuine anomalies. Continuous feedback loops powered by AI refine test suites over time, improving efficiency and accuracy.

The Future of QA Engineering

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