SKILL.md
Test Data Generation
Table of Contents
- [Overview](#overview)
- [When to Use](#when-to-use)
- [Quick Start](#quick-start)
- [Reference Guides](#reference-guides)
- [Best Practices](#best-practices)
Overview
Test data generation creates realistic, consistent, and maintainable test data for automated testing. Well-designed test data reduces test brittleness, improves readability, and makes it easier to create diverse test scenarios.
When to Use
- Creating fixtures for integration tests
- Generating fake data for development databases
- Building test data with complex relationships
- Creating realistic user inputs for testing
- Seeding test databases
- Generating edge cases and boundary values
- Building reusable test data factories
Quick Start
Minimal working example:
// tests/factories/userFactory.js
const { faker } = require("@faker-js/faker");
class UserFactory {
static build(overrides = {}) {
return {
id: faker.string.uuid(),
email: faker.internet.email(),
firstName: faker.person.firstName(),
lastName: faker.person.lastName(),
age: faker.number.int({ min: 18, max: 80 }),
phone: faker.phone.number(),
address: {
street: faker.location.streetAddress(),
city: faker.location.city(),
state: faker.location.state(),
zip: faker.location.zipCode(),
country: "USA",
},
role: "user",
isActive: true,
createdAt: faker.date.past(),
...overrides,
};
}
// ... (see reference guides for full implementation)Reference Guides
Detailed implementations in the references/ directory:
Guide
Contents
Factory Pattern for Test Data
Builder Pattern for Complex Objects
Builder Pattern for Complex Objects
Fixtures for Integration Tests
Fixtures for Integration Tests
Realistic Data Generation
Best Practices
✅ DO
- Use faker libraries for realistic data
- Create reusable factories for common objects
- Make factories flexible with overrides
- Generate unique values where needed (emails, IDs)
- Use builders for complex object construction
- Create fixtures for integration test setup
- Generate edge cases (empty strings, nulls, boundaries)
- Keep test data deterministic when possible
❌ DON'T
- Hardcode test data in multiple places
- Use production data in tests
- Generate truly random data for reproducible tests
- Create overly complex factory hierarchies
- Ignore data relationships and constraints
- Generate massive datasets for simple tests
- Forget to clean up generated data
- Use the same test data for all tests