AI-Generated Tests

Overview

AI-generated tests help you get to a usable baseline quickly. The typical flow is:

1. Import an OpenAPI/Swagger spec.
2. Generate a baseline set of test cases (happy path + key negatives).
3. Review, edit, and remove cases so they match your business rules.
4. Parameterize auth and data.
5. Run in CI as a quality gate.

What AI generation is good for

• fast onboarding for a new API or feature area
• expanding coverage with edge cases and invalid inputs
• producing consistent starting assertions (status codes, schema checks)

What you still need to provide

AI can’t know your environment or data constraints. For stable runs you still need:

• base URL + authentication configuration
• test data strategy
• environment variables and reusable parameters

Related: Configuration fundamentals and Test configuration.

Best practices

• Keep the spec up to date—generation quality depends on contract quality.
• Start with read-only endpoints to validate auth and connectivity.
• Treat AI output as a draft: refine assertions and business logic.
• Run small scopes first, then expand.

Related articles

• Test case
• Test run
• Test run pack
• AI settings