Test Data Generation

Test Data Generation

Table of Contents

• Overview
• When to Use
• Quick Start
• Reference Guides
• 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:

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