brianvoe/gofakeit
Gofakeit
gofakeit is a Go library for creating realistic synthetic datasets and populating Go structs with mock information. It functions as a deterministic data generator, allowing for seedable random number generation to ensure reproducible datasets for software testing.
The project distinguishes itself by providing a mock data API server that exposes generation functions as HTTP endpoints and a synthetic dataset exporter for producing files in CSV, JSON, and XML formats. It also includes a command-line interface for generating mock data directly from the terminal.
The library covers a wide array of generation domains, including personal identities, financial identifiers, security credentials, and software metadata. It provides specialized tools for simulating commerce, nature, and media, alongside logic utilities for slice shuffling and regular expression-based string generation.
The system supports custom data generators via interface-based extensions and utilizes reflection for automated struct population.
Features
- Mock Data Generators - Provides a comprehensive library for generating realistic synthetic data for software testing and development.
- Struct Population Tools - Fills Go data structures with random values based on types and tags to simplify object creation.
- Person Data Generators - Generates realistic human-centric personal data including names, biographies, and ethnicities.
- Random Number Generator Seeding - Allows initialization of generators with specific seeds to produce reproducible datasets.
- Seedable Generators - Provides seedable random number generation to ensure deterministic and reproducible synthetic datasets.
- Go Mocking Libraries - Provides a set of Go functions and reflection-based tools to produce synthetic values and populate Go structs.
- Deterministic - Ensures reproducible mock datasets through a seedable random number generator for consistent testing.
- Struct Generators - Fills complex data structures and struct fields with mock values using reflection and field tags.
- Reflection-Based Population - Automatically populates Go structs with synthetic data by inspecting fields and tags at runtime.
- Data Generation Frameworks - Provides a modular framework for registering and implementing user-defined custom data generators.
- Deterministic Pure Testing - Uses seeds to produce reproducible mock datasets for consistent software testing and debugging.
- Random Number Generation - Produces synthetic numeric values including integers, floats, and custom ranges.
- Mock Dataset Exporters - Generates and exports fake information into CSV, JSON, and XML files for testing environments.
- Synthetic Financial Data - Produces synthetic financial identifiers like CUSIP and ISIN with valid checksums.
- Structured JSON Generation - Produces randomly structured JSON objects or arrays based on specified fields and types.
- Synthetic Data Exporters - Exports generated synthetic datasets directly into CSV, JSON, and XML formats for system integration.
- CLI Mock Data Generators - Provides a command-line interface for generating mock data directly from the terminal.
- Frontend API Mocking - Exposes data generation functions as HTTP endpoints to simulate backend responses for frontend development.
- Multi-Format Data Exports - Exports generated mock datasets directly into various structured formats including CSV, JSON, and XML.
- Template String Interpolation - Implements string interpolation to replace placeholders in templates with randomized mock values.
- Random Date & Time Generators - Produces random timestamps, date components, and range-restricted values for temporal testing.
- Password Generators - Generates random password strings with configurable length and character complexity.
- Random Identifier Generators - Creates unique random strings and UUIDs to serve as primary keys in mock databases.
- Credit Card Generators - Produces realistic mock credit card numbers, expiration dates, and CVV codes.
- CSV Dataset Generation - Provides capabilities to create synthetic tabular datasets with configurable delimiters and row counts in CSV format.
- Fixed Width Dataset Generation - Produces tables of random data formatted for fixed width files based on precise field definitions.
- Location Data Generation - Creates synthetic physical addresses, cities, zip codes, and geographic coordinates for testing.
- Text and String Generation - Creates random strings that conform to specified regular expression patterns.
- API Mocking Servers - Ships a built-in HTTP server that exposes generation functions as endpoints for requesting customized mock values.
- Developer Utilities - Random data generator for testing and development.
- Testing Frameworks - Random data generator.
- General Utilities - Random data generator for testing.
Star history
How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
