Section 070 · Chapter 9, Generated Code Changes the Job
Halting, Gödel, and the Limits of Testing AI-Generated Code
Some limits are not tooling problems. They are built into computation, logic, and the difference between proof and evidence.
What to do
- Use formal verification where scope is narrow and specifications are stable, but pair it with runtime guards, resource limits, trace monitoring, property-based tests, fuzzing, and production feedback.
- Define runnable checks that exercise generated code, halting problem, and halting godel limits generated code.
- Set acceptable outcomes and blocker failures for generated code, halting problem, and halting godel limits generated code before running the evaluation.
Evidence to preserve
- Preserve the inputs, versions, configurations, raw outcomes, and results for generated code, halting problem, halting godel limits generated code needed to reproduce work on Halting, Gödel, and the Limits of Testing AI-Generated Code.
- Report results for generated code, halting problem, halting godel limits generated code by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
Use formal verification where scope is narrow and specifications are stable, but pair it with runtime guards, resource limits, trace monitoring, property-based tests, fuzzing, and production feedback. Theory is a warning against overconfidence, not an excuse for vague testing. It explains why validation must be layered.
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Cite this page
Jason Arbon. "Halting, Gödel, and the Limits of Testing AI-Generated Code." Testing AI Knowledge Edition, section 70.
https://jarbon.ai/testing-ai/knowledge/ch070-halting-godel-limits-generated-code.html