Data validation rules in Testing Fundamentals - Framework Patterns

Framework Mode - Data validation rules

Folder Structure

  data-validation-framework/
  ├── config/
  │   ├── environments.json       # Environment-specific settings
  │   └── validation-rules.json   # Centralized data validation rules
  ├── src/
  │   ├── validators/             # Validation logic modules
  │   │   ├── emailValidator.js
  │   │   ├── phoneValidator.js
  │   │   └── dateValidator.js
  │   ├── utils/                  # Helper functions
  │   │   └── formatters.js
  │   └── dataLoaders/            # Data input and parsing
  │       └── csvLoader.js
  ├── tests/                      # Test cases for validation rules
  │   ├── emailValidation.test.js
  │   ├── phoneValidation.test.js
  │   └── dateValidation.test.js
  ├── reports/                    # Test execution reports
  ├── package.json                # Project dependencies and scripts
  └── README.md
  

Test Framework Layers

• Config Layer: Holds environment settings and validation rules in JSON files for easy updates.
• Validator Layer: Contains reusable validation functions for different data types (email, phone, date).
• Data Loader Layer: Responsible for loading and parsing input data from files or sources.
• Test Layer: Automated test scripts that apply validation rules to sample or real data and assert correctness.
• Utility Layer: Helper functions for formatting, logging, or common operations used by validators and tests.

Configuration Patterns

• Centralized Rules: Store all validation rules in validation-rules.json for easy maintenance and updates without code changes.
• Environment Settings: Use environments.json to define different data sources or parameters per environment (dev, test, prod).
• Parameterization: Tests read config files at runtime to adapt validation logic dynamically.
• Secrets Management: Keep sensitive data like credentials out of validation rules; use environment variables or secure vaults.

Test Reporting and CI/CD Integration

• Use test runners (e.g., Jest, Mocha) that generate clear pass/fail reports with details on which validation rules failed.
• Integrate with CI/CD pipelines (GitHub Actions, Jenkins) to run validation tests automatically on code changes.
• Generate HTML or JSON reports stored in reports/ folder for easy review by team members.
• Set alerts or notifications on validation failures to quickly fix data issues.

Best Practices for Data Validation Rules Framework

• Single Source of Truth: Keep validation rules centralized to avoid duplication and inconsistencies.
• Reusable Validators: Write small, focused validation functions that can be combined or reused across tests.
• Clear Error Messages: Validation failures should provide descriptive messages to help identify data issues quickly.
• Automate Early: Run validation tests early in the data pipeline to catch errors before they propagate.
• Version Control: Track changes to validation rules and tests in source control for audit and rollback.

Self Check

Where in this framework structure would you add a new validator for checking postal codes?

Key Result

Centralize validation rules and use reusable validator functions tested automatically with clear reporting.