When Software Starts Thinking [Testμ 2026]

At Testμ Conf 2026, QualiZeal’s Chinmay Kothari argued that AI quality engineering must move beyond deterministic pass/fail testing because generative systems can produce differing yet plausible answers to the same prompt, introducing risks such as hallucination, agent-chain drift, bias, privacy exposure, and unsafe behavior. He proposed RMTE, a four-step approach that identifies risks for each trust attribute, defines measurable metrics, tests against ground-truth data, and preserves evidence for auditability, with seven attributes named as reliability, safety, explainability, security, privacy, faithfulness, and human control. His examples distinguish citation sufficiency from citation accuracy, advocate testing agent architectures layer by layer rather than only end-to-end, and frame release decisions around thresholds and overall trust rather than binary correctness. The session also acknowledged that perfect trust and exhaustive testing are unattainable, while presenting a future product concept that would generate a trust score. Several unresolved issues remained, including inconsistent claims about NIST’s number of trust attributes, conflicting threshold examples, and tension between an absolute requirement that every attribute pass and a weighted composite-score model.

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