Test Data Generation
Overview
This skill provides guidance and reusable patterns for creating robust and maintainable test data using factory libraries. It focuses on common patterns used in modern testing workflows, with examples primarily for Python (using factory_boy) and JavaScript (using polly.js).
Following these patterns helps create tests that are easier to read, write, and maintain.
Core Concepts
The main workflow is to define factories for your data models and then use them in your tests to create instances of those models.
This skill is organized by topic. Refer to the relevant document for detailed patterns and examples.
1. Basic Factory and Trait Patterns
For defining basic factories and creating variations using traits (e.g., an active user vs. a suspended user). This is the best place to start.
See [references/factory-patterns.md](./references/factory-patterns.md) for detailed examples.
2. Handling Relationships
For creating data with relationships, such as a user who has many posts, or posts that belong to a category (one-to-many, many-to-one, many-to-many).
See [references/relationship-patterns.md](./references/relationship-patterns.md) for detailed examples.
3. Unique Values and Realistic Data
For ensuring data uniqueness using sequences and for generating realistic-looking data (names, emails, addresses) using Faker.
See [references/sequence-and-faker.md](./references/sequence-and-faker.md) for detailed examples.
4. Persistence Strategies
For understanding the difference between in-memory object generation and creating records in a test database.
See [references/persistence.md](./references/persistence.md) for detailed examples.
5. Data Cleanup
For strategies on how to clean up test data between test runs to ensure test isolation.
See [references/cleanup.md](./references/cleanup.md) for detailed examples.
