bchavez/Bogus
Bogus
Bogus is a fake data generator for .NET applications, including C#, F#, and VB.NET. It provides a deterministic mock data engine and an object configuration mapper to produce realistic profiles, addresses, and financial records.
The library differentiates itself through a localization data provider that generates region-specific identifiers across various international languages and locales. It ensures reproducibility across executions by using seed values to control the sequence of generated data.
The project covers wide-ranging data synthesis capabilities, including the generation of network identities, financial records, and random primitives. It includes tools for template string parsing, weighted distribution selection for random elements, and the creation of placeholder media links and text.
Developer tools are provided for real-time code analysis and validation to ensure all properties of a target class have assigned generation rules.
Features
- Mock Data Generators - Provides a comprehensive library for creating realistic fake datasets for C# and .NET applications.
- Seeded Generation - Ensures reproducible synthetic data sequences across executions using deterministic mathematical seeds.
- Object Mapping - Maps random data to class properties, interfaces, and protected members using defined generation rules.
- Regional Data Localization - Provides internationalized mock data to verify how applications handle different languages and region-specific formats.
- Synthetic Profile Generators - Creates realistic mock profiles for persons, addresses, and companies across multiple international locales.
- Seeding Utilities - Provides utilities to seed random number generators with fixed values to ensure data reproducibility.
- Deterministic - Ensures reproducible random datasets using seed values to maintain consistent results across test runs.
- .NET Data Generation Libraries - Implements a realistic mock data generation engine specifically for C#, F#, and VB.NET applications.
- Locale-Aware Data Generators - Generates synthetic data tailored to specific regional formats, languages, and cultural conventions across international locales.
- Mock Object Configuration Mappers - Maps random data to class properties, interfaces, and protected members based on defined generation rules.
- Localized Data Generation - Localizes generated data by selecting specific languages or regions from a wide array of supported locales.
- Test Case Generators - Generates deterministic and reproducible input data to validate application behavior and edge cases during development.
- Probabilistic Element Selection - Picks random elements from collections using weighted distributions to simulate realistic data frequency.
- Generation Rule Analysis - Includes a compiler-integrated analysis tool that provides real-time feedback on data generation code within an IDE.
- Template String Interpolation - Provides a template string parsing engine to populate placeholders with random data from mock datasets.
- Synthetic Network Data Generators - Generates fake network identifiers including IP addresses, MAC addresses, URLs, and user agent strings.
- Fluent Interfaces - Provides a fluent API for defining data generation rules through method chaining to reduce boilerplate.
- Random Value Generators - Generates random basic types including strings, integers, booleans, GUIDs, and hexadecimal values.
- Template Interpolation - Parses strings with placeholders and interpolates them with values from mock datasets.
- Random Date & Time Generators - Generates random dates, times, and timespans using relative offsets and specific time-of-day ranges.
- Object Shape Validation - Ensures all properties of a target class have an assigned generation rule to prevent missing mock data.
- Randomized Selection Algorithms - Picks random items or subsets from collections using probabilistic weighted selection algorithms.
- Reflection-Based Property Accessors - Uses runtime reflection to dynamically map generation rules to class properties, including protected members.
- Bulk Generation Rules - Allows applying multiple property generation rules in a single action to reduce configuration repetition.
- Financial Record Generation - Generates mock credit card numbers, IBANs, BIC codes, and cryptocurrency addresses for testing.
- Synthetic Identity Generators - Generates region-specific identifiers such as national ID numbers and tax codes for various countries.
- Test Data Generation - Simple library for generating fake data for objects.
- Testing - Fake data generator.
- Test-Frameworks - Library for generating fake data for testing.
