This is where fuzzing would be useful. We have an at-least-parity-bug-level oracle with the reference PostgreSQL implementation. Just build a generator of queries (both invalid and valid) and ensure the output matches. The yardstick is how many log10(queries) it can go on average before a discrepancy is found.
It’s 2026, so another basic technique that every team should add to their testing strategy (in addition to proven techniques like fuzzing) is agentic user simulation. Set up an AI agent with access to the product, and prompt it to use the thing in hundreds of realistic use cases, and to report any possible bugs. It will catch a lot of ‘blind spot’ bugs that were previously things that were only caught by humans.
Debug yourself of the AI hype^. Besides config, SQL implementations have a very limited textual API surface of accepting queries. Putting whole layers of UI and agents around it is really inefficient way of finding edge cases. I wouldn't call that basic, nor the most comprehensive option. Fuzzing at the query layer is far more computationally efficient and effective.
A truly stochastic method is more likely to hit against edge cases, rather than an agent that tends to towards idiomatic solutions and that is trained against a corpus of existing software, and burns millions of tokens/watts spinning its wheels.
^ There's plenty of business value to be found in agentic AI without reaching for it for every solution. I'd even posit agentic AI is even better when paired with focused old-fashioned squishy-brained software engineering in the loop.
you don't have to compare whether stochastic fuzzing or agentic testing is the best. You can just do both of them. They both have enough ROI to be worth the trouble. That's why I said "in addition to".
