MockLock Fake Data Generation Suite

Overview

The MockLock Fake Data Generation Suite provides developers with tools to create realistic test data for development and testing environments. This suite is essential for creating authentic-looking test data without using real personally identifying information.

Features

The Test Data Suite includes four primary generators:

1. Fake Person Generator
2. Fake ID Generator
3. Fake Passport Generator
4. Test Card Data Generator

Fake Person Generator

Purpose

Generate complete fictional user profiles with realistic personal details for testing user-centric applications.

Generated Data

• Personal information (name, age, gender)
• Contact details (email, phone number)
• Address information (street, city, postal code)
• Nationality
• Generated username and password

Use Cases

• Testing user registration flows
• Populating databases with test users
• UI development for user profile pages
• Testing data validation rules

How to Use

1. Open the Fake Person Generator
2. Customize nationality settings if desired
3. Click "Generate" for a complete fictional profile
4. Copy individual fields or the entire profile as needed

Fake ID Generator

Purpose

Create realistic-looking identification document data for testing identity verification systems.

Generated Data

• ID number following country-specific formats
• Issue date and expiration date
• Personal details (name, date of birth, gender)
• Address information
• Document-specific details

Use Cases

• Testing KYC (Know Your Customer) processes
• Developing identity verification systems
• Testing document scanner applications
• Simulating user verification workflows

How to Use

1. Select the ID type and country
2. Customize fields if needed or use randomized data
3. Generate the ID document data
4. Copy or download the generated information

Implementation Details

The ID generator uses country-specific algorithms to create valid-format IDs. For example, South African ID numbers follow the format:

YYMMDD GSSS CAZ

Where:

• YYMMDD: Date of birth
• G: Gender (0-4 female, 5-9 male)
• SSS: Sequence number
• C: Citizenship (0 for SA citizen)
• A: Usually 8 (for pre-1994 IDs, 0 or 1)
• Z: Control digit

Fake Passport Generator

Purpose

Generate realistic passport data for international testing scenarios.

Generated Data

• Passport number
• Personal information (name, date of birth, place of birth)
• Issue and expiry dates
• Nationality/issuing country
• Machine Readable Zone (MRZ) data

Use Cases

• Testing international travel applications
• Developing passport scanning functionality
• Testing international user workflows
• Simulating travel booking systems

How to Use

1. Select the passport country of issue
2. Generate a random passport or customize specific fields
3. View the generated passport data including MRZ codes
4. Copy individual fields or export the complete data

Security Notes

• The generated passports are for testing only and follow the correct format but are not valid documents
• The data includes checksums that will pass basic validation
• A clear "SPECIMEN" or "TEST DATA" watermark is included in any visual representation

Test Card Data Generator

Purpose

Create valid-format credit card numbers for testing payment systems without using real financial data.

Generated Data

• Card number (following Luhn algorithm)
• Card brand (Visa, Mastercard, Amex, etc.)
• Expiration date
• Security code (CVV/CVC)
• Cardholder name

Use Cases

• Testing payment processing systems
• Developing e-commerce checkout flows
• QA testing for financial applications
• Testing payment gateway integrations

How to Use

1. Select the desired card brand
2. Generate random card details
3. Copy the card information for testing
4. Use the test mode of your payment processor with this data

Implementation Details

The card numbers are generated following the Luhn algorithm to ensure they pass basic validation checks:

javascriptfunction isValidCardNumber(number) {
  // Remove spaces and non-digit characters
  number = number.replace(/\D/g, '');
  
  // Check if empty or not the right lengthpan> empty or not the right length
  if (!number || number.length < 13) return false;
  
  // Luhn algorithm implementation
  let sum = 0;
  let doubled = false;
  
  // Loop through each digit from right to leftkeyword">from right to left
  for (let i = number.length - 1; i >= 0; i--) {
    let digit = parseInt(number.charAt(i));
    
    // Double every second digit
    if (doubled) {
      digit *= 2;
      if (digit > 9) digit -= 9;
    }
    
    sum += digit;
    doubled = !doubled;
  }
  
  // Valid if sum is a multiple of 10>if sum is a multiple of 10
  return sum % 10 === 0;
}

Best Practices

General Guidelines

• Always use test data in development/testing environments only
• Never attempt to use generated data for actual identity verification
• Include clear markers in your test database to identify test data
• Use different test data profiles for different test scenarios

Data Protection

• Even though the data is fictitious, handle it according to your data protection policies
• Do not expose generated test data in public repositories
• Sanitize logs that might contain generated test data
• Use data anonymization when converting production data for testing

Testing Strategies

• Create a library of test data profiles for consistent testing
• Test edge cases by customizing the generated data
• Use generated data in automated testing scripts
• Maintain a set of known test data for regression testing

Technical Implementation

The Fake Data Generation Suite is built using:

• React components for the user interface
• JavaScript algorithms for data generation
• Country-specific rule implementations
• Data validation to ensure realistic outputs

Each generator uses specialized algorithms to create data that appears authentic and passes basic validation, while ensuring the data doesn't match any real person's information.