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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.
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설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
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npx skills add https://github.com/proffesor-for-testing/sentinel-api-testing --skill test-data-management명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
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SOC 직업 분류 기준
SKILL.md 표시 중
| name | 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. |
| category | specialized-testing |
| priority | high |
| tokenEstimate | 1000 |
| agents | ["qe-test-data-architect","qe-test-executor","qe-security-scanner"] |
| implementation_status | optimized |
| optimization_version | 1 |
| last_optimized | 2025-12-02T00:00:00.000Z |
| dependencies | [] |
| quick_reference_card | true |
| tags | ["test-data","faker","synthetic","gdpr","pii","anonymization","factories"] |
<default_to_action> When creating or managing test data:
Quick Data Strategy:
Critical Success Factors:
| Type | When | Size |
|---|---|---|
| Minimal | Unit tests | 1-10 records |
| Realistic | Integration | 100-1000 records |
| Volume | Performance | 10k+ records |
| Edge cases | Boundary testing | Targeted |
| Technique | Use Case |
|---|---|
| Synthetic | Generate fake data (preferred) |
| Masking | j***@example.com |
| Hashing | Irreversible pseudonymization |
| Tokenization | Reversible with key |
import { faker } from '@faker-js/faker';
// Seed for reproducibility
faker.seed();
() {
{
: faker..(),
: faker..(),
: faker..(),
: faker..(),
: faker..(),
: {
: faker..(),
: faker..(),
: faker..()
},
: faker..()
};
}
users = .({ : }, generateUser);
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.email ?? faker.internet.email(),
role: this.user.role ?? 'customer',
...this.user
} as User;
}
}
// Usage
const admin = new UserBuilder().asAdmin().withEmail('admin@test.com').build();
const customer = new UserBuilder().asCustomer().build();
// Masking
function maskEmail(email) {
const [user, domain] = email.split('@');
return `${user[0]}***@${domain}`;
}
// john@example.com → 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
}));
// 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: 'test@example.com'
});
expect(user.id).toBeDefined();
// Automatic rollback after test - no cleanup needed
});
// 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}`);
}
}
// 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");
aqe/test-data-management/
├── schemas/* - Data schemas
├── generators/* - Generator configs
├── anonymization/* - PII handling rules
└── fixtures/* - Reusable fixtures
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'
});
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.