OpenAPI specifications describe what your API should do. Test suites verify that it actually does it. Bridging this gap manually is slow, and teams often only write tests for the happy path, leaving edge cases and error scenarios uncovered.

The AI Approach

Feed your OpenAPI spec to an LLM and ask it to generate test cases for each endpoint. The model understands parameter types, required fields, and response schemas well enough to produce tests that cover valid requests, invalid inputs, missing required fields, boundary values, and authentication edge cases.

Three-Step Build

  1. Parse the spec - Extract endpoint definitions, request schemas, response schemas, and authentication requirements from your OpenAPI spec.
  2. Generate tests - For each endpoint, prompt the LLM to generate test cases covering: valid requests with expected responses, missing required fields, invalid data types, boundary values for numeric fields, and unauthorized access attempts.
  3. Output as runnable tests - Format the generated tests in your preferred framework (pytest, Jest, Postman collections) and run them against a test environment.

Where It Breaks

The LLM does not understand your business logic. It can test that a 400 response comes back for a missing field, but it cannot know that creating a user with a duplicate email should return 409 specifically. Generated tests need human review to add business-rule-specific assertions.

The Production Path

Use AI-generated tests as a baseline that covers structural correctness, then layer on hand-written tests for business logic. Regenerate the baseline tests whenever the API spec changes. Integrate into CI so generated tests run on every build.