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24 posts in the last 7 days
Verifying AI-Generated Tests in Python
Developers are addressing the false confidence of green test suites generated by AI coding agents, which often write both code and validation checks without real verification. Discussions focus on shifting from naive pass rates to rigorous techniques like mutation testing, assumption ledgers, and negative golden sets to catch silent agent failures.
Key Areas of Focus:
- How can we detect when AI agents invent arguments or pass false test metrics?
- Why is a green test suite written by the same coding agent insufficient for production quality?
- What metrics, such as assumption density or mutation testing, should replace standard pass rates?
Active 1 day ago
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