test-data-bot generates fake data for unit tests, inspired by Factory Bot. It provides a builder API to create objects with customizable fields and behavior. The library is written in TypeScript and can be used with JavaScript. It aims to improve test consistency by providing realistic data.
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Flexible Field Definition: Easily define fields with various data types and constraints.
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ID Sequencing: Generates unique, incrementing IDs for objects.
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Random Value Selection: Allows picking values randomly from a predefined list.
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Per-Build Configuration: Enables creation of objects with constant values or using external libraries.
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Overrides: Provide specific values for individual fields during object creation.
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Map Function: Modifies generated objects after they are created.
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Developer Experience: Provides a clean and intuitive API for building and using data factories.
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Data Generation: Creates realistic fake data for testing purposes.
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TypeScript Support: Written in TypeScript with type safety for improved code quality.
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Flexible Configuration: Offers various options for defining field types and constraints.
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Randomization: Supports random value selection from lists or functions.
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Sequence Generation: Provides unique ID generation for each object.
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Customizable Behavior: Allows applying functions to modify objects after creation.
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Test Data Consistency: Ensures consistent and reproducible test data.
The project has been active since 2018 and has a history of releases, though it is currently no longer maintained. The repository has a reasonable number of stars and forks, indicating past community interest. While recent commits are lacking, the documentation provides enough details for basic usage. A newer version @jackfranklin/test-data-bot has been released and is recommended for new projects.
test-data-bot benefits developers by enabling the creation of consistent and realistic test data, improving the reliability and effectiveness of unit tests. It is particularly valuable for teams seeking to automate data generation and ensure that tests are based on accurate representations of real-world scenarios. It provides a more efficient alternative to manual data creation or using hardcoded values.