This whitepaper explains how Generative AI can reshape the Software Testing Life Cycle by turning requirements into active inputs for testing rather than static documents. It targets major pain points in traditional testing: manual requirement interpretation, repetitive scenario and test-case creation, and shallow pass/fail reporting. The proposed layered architecture uses LLMs, LangChain, and GitHub Copilot to refine BRDs and specs, generate scenarios and detailed test cases, build reusable automation frameworks, convert tests into executable scripts, and analyze test reports for flakiness and feature stability. The paper concludes that AI works best as a co-pilot with human review, improving speed, traceability, quality, and scalability without replacing engineering judgment.

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