bchavez/Bogus

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.
• Testing Frameworks - Library for generating fake data for testing.

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