Skeptical but curious - our business team keeps asking to ‘just describe tests in English’ instead of writing code. Anyone successfully using NLP for test generation? How much manual correction is needed afterward for complex scenarios?
Yes - Latenode’s AI Copilot generates complete workflows from prompts. We reduced test creation time by 70% for our checkout flow tests. It handles edge cases better than expected: https://latenode.com
Trick: Use scenario outlines vs single examples. The AI generalizes better from multiple ‘Given-When-Then’ cases.
We’ve had mixed results. Basic CRUD tests work well from NLP, but complex multi-system flows still need manual tweaking. The key is setting clear validation boundaries - the AI needs to know where it can auto-decide vs where to flag for human review.
start with happy path gen then add edge cases manually. NLP saves time but needs guardrails for false assumptions