Designing Quality Frameworks for AI-Generated Code [Testμ 2026]

At Testμ Conf 2026, Infosys Industry Principal Neelmani Verma argued that AI-generated code challenges traditional quality assurance not because it is inherently poor, but because familiar signals such as clean syntax, passing tests, and confident presentation may not demonstrate that the code reflects business intent. Using a loyalty-discount function that mistakenly applied discounts to shipping and tax after a later data-model change, she showed how tests generated alongside code can mirror the same flawed assumptions rather than serve as independent oracles. Drawing on zero-trust security principles, Verma proposed continuous verification built on specification-derived assertions, risk-based blast-radius controls, scheduled or drift-triggered re-attestation, variable review depth, and an evidence ledger recording model versions, policies, reviews, and prior drift events. She suggested prioritizing specification coverage and attestation freshness over conventional pass rates and line coverage, assigning ownership for risk tiers and re-verification triggers, and treating model updates more like staffing changes than ordinary code changes. Her framework is presented as an operating-model shift rather than a new testing-tool purchase and is intended to apply to human-written code as well as AI-assisted development.

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