Skip to content

overview.md

Latest commit

 

History

History
56 lines (45 loc) · 2.15 KB

overview.md

File metadata and controls

56 lines (45 loc) · 2.15 KB

Java Faker Codebase Overview

This is Java Faker, a data generation library for creating realistic fake data (names, addresses, phone numbers, etc.) for testing and development. It's a Java port of Ruby's Faker gem.

Project Info

  • Version: 1.0.3-SNAPSHOT
  • Build: Maven (with wrapper)
  • Java: 11+
  • License: Apache 2.0

Structure

src/main/java/com/github/javafaker/
├── Faker.java              # Main entry point (facade)
├── *.java                  # 93 data provider classes
├── service/                # Core infrastructure
│   ├── FakeValuesService   # Resolves YAML data, expressions
│   ├── FakeValues          # Loads locale YAML files
│   └── RandomService       # Random number generation
└── idnumbers/              # ID generators (SSN, Swedish IDs)

src/main/resources/
└── *.yml                   # 59 locale data files (en, de, fr, ja, etc.)

src/test/java/              # 111 test files

Key Components

Component Purpose
Faker Central facade with 93+ provider instances
FakeValuesService Resolves YAML expressions, handles locale chains
RandomService Random numbers, hex, UUIDs
Data Providers Name, Address, Internet, Company, Lorem, GameOfThrones, Pokemon, etc.

Dependencies

  • Guava (33.5.0) - Utilities
  • Commons Lang3 (3.20.0) - String utils
  • SnakeYAML (2.5) - YAML parsing
  • Generex (1.0.2) - Regex-to-string generation

How It Works

  1. Faker instantiates all provider classes
  2. Providers call faker.fakeValuesService().resolve("key")
  3. FakeValuesService looks up data in locale YAML files
  4. YAML expressions like #{Name.first_name} are evaluated dynamically
  5. Results can be transformed with numerify, letterify, regexify

Testing

  • 111 test classes using JUnit 4 + Mockito
  • Custom @Repeat annotation for multi-run tests
  • Integration tests in integration/ subdirectory
  • JaCoCo coverage with Coveralls integration

The codebase is well-organized with clear separation between data providers and the service layer that handles data loading and expression evaluation.