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How AI is reshaping software testing and where load testing still fits

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AI tools are increasingly used to draft tests, triage failures, maintain test suites, and explain results, while the tests that gate releases are best kept as versioned, reviewable code. The article argues that AI-generated tests need stronger validation, such as checking assertions, edge cases, and mutation scores, since a passing test does not guarantee defects are caught. Surveys cited in the piece suggest broad experimentation with generative AI in quality engineering but limited enterprise-wide scaling, with privacy, integration, and reliability concerns as main barriers. The piece also covers using AI to interpret load test results and connecting AI agents to testing workflows, and notes that testing AI-powered applications requires metrics such as time to first token and output validity.

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