A real-world evaluation of AI-written tests. We restored real bugs from git history to find out whether the generated tests could actually detect them.
Five practical techniques for trusting AI-written tests, learned from six months of relying on an AI-written test suite every day.
AI document consistency shapes behavior. Practical exercises to identify and fix documentation conflicts that make AI agents unreliable.
Can this prompt-driven test automation system scale with complex applications using lessons learnt loops?
Treat markdown test cases as source code that gets compiled into executable test automation through a defined 4 step process.
Can you transform unreliable agentic tests into deterministic, repeatable tests using a 'run twice' pattern?