qe-test-data-management

inclusion	auto
name	qe-test-data-management
description	Strategic test data generation, management, and privacy compliance. Use when creating test data, handling PII, ensuring GDPR/CCPA compliance, or scaling data generation for realistic testing scenarios.
tags	["test-data","faker","synthetic","gdpr","pii","anonymization","factories"]

Test Data Management

<default_to_action> When creating or managing test data:

1. NEVER use production PII directly
2. GENERATE synthetic data with faker libraries
3. ANONYMIZE production data if used (mask, hash)
4. ISOLATE test data (transactions, per-test cleanup)
5. SCALE with batch generation (10k+ records/sec)

Quick Data Strategy:

• Unit tests: Minimal data (just enough)
• Integration: Realistic data (full complexity)
• Performance: Volume data (10k+ records)

Critical Success Factors:

• 40% of test failures from inadequate data
• GDPR fines up to €20M for PII violations
• Never store production PII in test environments </default_to_action>

Quick Reference Card

When to Use

• Creating test datasets
• Handling sensitive data
• Performance testing with volume
• GDPR/CCPA compliance

Data Strategies

Type	When	Size
Minimal	Unit tests	1-10 records
Realistic	Integration	100-1000 records
Volume	Performance	10k+ records
Edge cases	Boundary testing	Targeted

Privacy Techniques

Technique	Use Case
Synthetic	Generate fake data (preferred)
Masking	j***@example.com
Hashing	Irreversible pseudonymization
Tokenization	Reversible with key

---

Synthetic Data Generation

import { faker } from '@faker-js/faker';

// Seed for reproducibility
faker.seed(123);

function generateUser() {
  return {
    id: faker.string.uuid(),
    email: faker.internet.email(),
    firstName: faker.person.firstName(),
    lastName: faker.person.lastName(),
    phone: faker.phone.number(),
    address: {
      street: faker.location.streetAddress(),
      city: faker.location.city(),
      zip: faker.location.zipCode()
    },
    createdAt: faker.date.past()
  };
}

// Generate 1000 users
const users = Array.from({ length: 1000 }, generateUser);

---

Test Data Builder Pattern

class UserBuilder {
  private user: Partial<User> = {};

  asAdmin() {
    this.user.role = 'admin';
    this.user.permissions = ['read', 'write', 'delete'];
    return this;
  }

  asCustomer() {
    this.user.role = 'customer';
    this.user.permissions = ['read'];
    return this;
  }

  withEmail(email: string) {
    this.user.email = email;
    return this;
  }

  build(): User {
    return {
      id: this.user.id ?? faker.string.uuid(),
      email: this.user. ?? faker..(),
      : .. ?? ,
      ....
    }  ;
  }
}


 admin =  ().().().();
 customer =  ().().();

---

Data Anonymization

// Masking
function maskEmail(email) {
  const [user, domain] = email.split('@');
  return `${user[0]}***@${domain}`;
}
// [e-mail] → j***@example.com

function maskCreditCard(cc) {
  return `****-****-****-${cc.slice(-4)}`;
}
// 4242424242424242 → ****-****-****-4242

// Anonymize production data
const anonymizedUsers = prodUsers.map(user => ({
  id: user.id, // Keep ID for relationships
  email: `user-${user.id}@example.com`, // Fake email
  firstName: faker.person.firstName(), // Generated
  phone: null, // Remove PII
  createdAt: user.createdAt // Keep non-PII
}));

---

Database Transaction Isolation

// Best practice: use transactions for cleanup
beforeEach(async () => {
  await db.beginTransaction();
});

afterEach(async () => {
  await db.rollbackTransaction(); // Auto cleanup!
});

test('user registration', async () => {
  const user = await userService.register({
    email: '[e-mail]'
  });
  expect(user.id).toBeDefined();
  // Automatic rollback after test - no cleanup needed
});

---

Volume Data Generation

// Generate 10,000 users efficiently
async function generateLargeDataset(count = 10000) {
  const batchSize = 1000;
  const batches = Math.ceil(count / batchSize);

  for (let i = 0; i < batches; i++) {
    const users = Array.from({ length: batchSize }, (_, index) => ({
      id: i * batchSize + index,
      email: `user${i * batchSize + index}@example.com`,
      firstName: faker.person.firstName()
    }));

    await db.users.insertMany(users); // Batch insert
    console.log(`Batch ${i + 1}/${batches}`);
  }
}

---

Agent-Driven Data Generation

// High-speed generation with constraints
await Task("Generate Test Data", {
  schema: 'ecommerce',
  count: { users: 10000, products: 500, orders: 5000 },
  preserveReferentialIntegrity: true,
  constraints: {
    age: { min: 18, max: 90 },
    roles: ['customer', 'admin']
  }
}, "qe-test-data-architect");

// GDPR-compliant anonymization
await Task("Anonymize Production Data", {
  source: 'production-snapshot',
  piiFields: ['email', 'phone', 'ssn'],
  method: 'pseudonymization',
  retainStructure: true
}, "qe-test-data-architect");

---

Agent Coordination Hints

Memory Namespace

aqe/test-data-management/
├── schemas/*            - Data schemas
├── generators/*         - Generator configs
├── anonymization/*      - PII handling rules
└── fixtures/*           - Reusable fixtures

Fleet Coordination

const dataFleet = await FleetManager.coordinate({
  strategy: 'test-data-generation',
  agents: [
    'qe-test-data-architect',  // Generate data
    'qe-test-executor',        // Execute with data
    'qe-security-scanner'      // Validate no PII exposure
  ],
  topology: 'sequential'
});

---

Related Skills

• database-testing - Schema and integrity testing
• compliance-testing - GDPR/CCPA compliance
• performance-testing - Volume data for perf tests

---

Remember

Test data is infrastructure, not an afterthought. 40% of test failures are caused by inadequate test data. Poor data = poor tests.

Never use production PII directly. GDPR fines up to €20M or 4% of revenue. Always use synthetic data or properly anonymized production snapshots.

With Agents: qe-test-data-architect generates 10k+ records/sec with realistic patterns, relationships, and constraints. Agents ensure GDPR/CCPA compliance automatically and eliminate test data bottlenecks.