qa-data-factory

name	qa-data-factory
description	Generate realistic test data using fixtures, factory patterns, and faker-based seeds for consistent and reproducible test environments.
output_dir	data/fixtures

QA Data Factory

Purpose

Generate realistic, consistent, and reproducible test data for automated and manual testing. Supports fixtures (static JSON/YAML), factory patterns (dynamic generation with overrides), faker-based seeds (realistic random data), and builder patterns (fluent API for complex objects). Produces fixtures, factory classes, seed scripts, and data cleanup utilities.

Trigger Phrases

• "Generate test data for [entity type]"
• "Create fixtures for [Users/Products/Orders]"
• "Factory pattern for test data"
• "Faker-based seed data"
• "Test data builder for [complex object]"
• "Realistic test data with locale [en_US/de_DE]"
• "Seed script for test database"
• "Data cleanup utilities for tests"

Data Generation Approaches

Approach	Use Case	Output
Fixtures	Static, version-controlled data; predictable	JSON, YAML, CSV files
Factory pattern	Dynamic generation with overrides; per-test customization	Factory classes (TypeScript/Python)
Faker-based seeds	Realistic but random; locale-aware	Seed scripts, inline generation
Builder pattern	Complex nested objects; fluent API	Builder classes

See references/factory-patterns.md for implementation details.

TypeScript Tools

Tool	Purpose
@faker-js/faker	Realistic random data (names, emails, addresses, dates)
fishery	Factory pattern with sequences and traits
factory.ts	TypeScript factories with associations
Test data builders	Fluent API for complex objects

Python Tools

Tool	Purpose
Faker	Realistic random data; locale support
factory_boy	Factory pattern with sequences, subfactories
pytest-factoryboy	Pytest integration; fixtures from factories
model_bakery	Django model factories; minimal boilerplate

See references/faker-guide.md for usage patterns.

Data Types Supported

Type	Examples	Locale-aware
Users	name, email, username, password hash	Yes (names, formats)
Products	SKU, title, price, category	Yes (currency, formats)
Orders	order ID, items, totals, status	Yes (dates, currency)
Addresses	street, city, postal code, country	Yes
Payment info	card numbers (test), expiry, CVV	Test card patterns
Dates	birthdate, created_at, expiry	Yes (formats)
Emails	valid format, domain	Yes
Names	first, last, full	Yes (locale-specific)

Output Artifacts

1. Test data fixtures — Static JSON/YAML/CSV for predictable scenarios
2. Factory classes — TypeScript or Python factories with overrides
3. Seed scripts — Scripts to populate test DB with faker data
4. Data cleanup utilities — Teardown scripts, truncate/reset helpers

Workflow

1. Identify data needs — Entity types, required fields, relationships
2. Choose approach — Fixtures vs factory vs faker based on use case
3. Select tools — Match project stack (TS/Python, framework)
4. Generate artifacts — Fixtures, factories, seed scripts
5. Add cleanup — Teardown for isolation between test runs

Scope

Can do (autonomous):

• Generate fixtures, factories, seed scripts for specified entities
• Use Faker/faker-js for realistic locale-aware data
• Produce TypeScript (fishery, factory.ts) and Python (factory_boy, Faker) code
• Create data cleanup utilities
• Reference factory-patterns and faker-guide

Cannot do (requires confirmation):

• Add new dependencies without approval
• Modify production schemas or migrations
• Generate data for proprietary/restricted formats

Will not do (out of scope):

• Execute seed scripts or modify databases directly
• Generate production data or PII
• Bypass data validation or security constraints

References

Topic	File
Factory pattern implementations (TS, Python)	references/factory-patterns.md
Faker library usage for realistic data	references/faker-guide.md

Quality Checklist

• Generated data matches entity schema/contract
• Locale specified when names/addresses/formats matter
• No real PII or production-like secrets
• Fixtures are deterministic; faker seeds use configurable seed for reproducibility
• Cleanup utilities included for test isolation
• Factory overrides documented for common scenarios

Troubleshooting

Symptom	Likely Cause	Fix
Data conflicts between tests	Shared state; no cleanup	Add teardown; use unique IDs/emails per test
Unrealistic data	Default faker; wrong locale	Set locale; use appropriate faker providers
Factory too verbose	Over-specification	Use sensible defaults; override only when needed
Seed non-reproducible	Random seed not set	Use faker.seed(123) or equivalent
Schema mismatch	Generated fields don't match API/DB	Align with OpenAPI/DB schema; validate output