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AI-Generated Code Needs Rigorous Testing Before It Is Production-Ready

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AI coding assistants can produce clean, functional-looking code within seconds, but experts warn that appearance does not guarantee safety or correctness. Testing consistently exposes logic errors, missing edge cases, security vulnerabilities, hallucinated dependencies, and weak error handling that survive casual review. Because AI models predict code from patterns rather than understanding business rules or security requirements, they silently fill gaps with unverified assumptions. Issues such as SQL injection risks, invented package names, and swallowed exceptions typically surface only under structured testing, often after deployment when fixes are most costly. Developers and engineering leaders are advised to treat AI output as a draft requiring thorough review and testing before it reaches production.

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