Cursor
testing
Mock Data Schema Mismatch in Cursor-Generated Tests
Tests generated by Cursor use mock data objects that don't match the actual schema of your database models, API responses, or TypeScript interfaces. The mocks have missing required fields, wrong data types, extra properties that don't exist, or outdated schema versions that don't reflect recent changes to your models.
This schema mismatch means tests pass with incorrect data structures, giving false confidence. A test might verify that a function handles a user object correctly, but the mock user is missing the role field that your actual code checks — so the test passes while the real code would fail. Alternatively, tests fail because the mock triggers validation errors from mismatched types.
The problem is insidious because it's not always obvious. Tests may pass for months until a code path that accesses the missing or mistyped field is finally triggered in production.
Error Messages You Might See
ValidationError: "role" is required
TypeError: Cannot read properties of undefined (reading 'email')
Expected object to match schema but received extra keys: ["oldField"]
ZodError: Required at "createdAt"
AssertionError: expected { id: '123' } to deeply equal { id: 123 }
Common Causes
- Cursor hallucinated the data schema — The AI generated plausible-looking mock data that doesn't match your actual model definitions
- Schema evolved after tests were written — New required fields were added to the database or API, but the mock objects in tests weren't updated
- Partial mocks missing required fields — Mocks only include fields used in the test, missing required fields that cause validation errors in helper functions or middleware
- Wrong data types in mocks — Mock uses a string for an ID field that's actually a number, or a plain object where a Date instance is expected
- API response shape different from database model — Cursor used the database model shape for an API response mock (or vice versa), but the API transforms the data (camelCase vs snake_case, nested vs flat)
How to Fix It
- Create a single source of truth for mock data — Define factory functions or fixtures that generate mock data based on your actual TypeScript interfaces or Zod schemas, not hand-crafted objects
- Use schema validation in tests — Validate mock data against your Zod, Joi, or TypeScript schemas before using it in tests:
const mockUser = UserSchema.parse(mockData) - Generate mocks from types automatically — Use libraries like @anatine/zod-mock, intermock, or fishery to auto-generate mock data from your type definitions
- Review every mock field against the real model — Open your model/interface definition side-by-side with the mock and verify every field name, type, and required/optional status
- Add snapshot tests for API responses — Create snapshot tests that capture the actual shape of API responses, so any schema change is caught immediately
- Centralize mock factories — Create a
tests/factories/directory with factory functions for each model. Update them in one place when schemas change
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Get HelpFrequently Asked Questions
How do I keep mock data in sync with my models?
Use factory functions that derive from your actual types. Libraries like fishery or @anatine/zod-mock generate mock data directly from your TypeScript interfaces or Zod schemas, ensuring they stay in sync automatically.
Should I use real database data in tests?
For unit tests, use mock data for speed and isolation. For integration tests, use a test database with seed data. Never use production data in tests due to privacy concerns and non-deterministic results.