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Testing Strategy

This guide outlines the testing strategy for the Bluefly LLM ecosystem.

Testing Levels

The Bluefly LLM ecosystem employs a comprehensive testing approach with multiple levels:

Unit Testing

Unit tests verify the functionality of individual components in isolation. Each project should maintain a suite of unit tests with high coverage.

  • Framework: Jest for JavaScript/TypeScript, pytest for Python
  • Coverage Target: 80% or higher
  • Location: tests/unit/ directory in each project
  • Running: npm test or pytest tests/unit/

Integration Testing

Integration tests verify that different components work together correctly.

  • Framework: Jest with supertest for API testing
  • Coverage: Focus on critical integration points
  • Location: tests/integration/ directory in each project
  • Running: npm run test:integration

End-to-End Testing

End-to-end tests verify complete user workflows across multiple components.

  • Framework: Cypress or custom scripts
  • Coverage: Key user journeys and scenarios
  • Location: tests/e2e/ directory
  • Running: npm run test:e2e

Performance Testing

Performance tests ensure the system meets performance requirements.

  • Framework: k6 or JMeter
  • Location: tests/performance/ directory
  • Running: npm run test:performance

Test Data Management

Test Fixtures

Reusable test data is stored in fixture files:

  • JSON files for structured data
  • Text files for unstructured inputs
  • Mock responses for external services

Test Environments

Tests can be run in different environments:

  • Local: Developer's machine
  • CI: GitLab CI/CD pipelines
  • Staging: Pre-production environment

Testing Best Practices

Writing Effective Tests

  • Follow the AAA pattern (Arrange, Act, Assert)
  • Test both success and failure cases
  • Keep tests independent and idempotent
  • Use descriptive test names

Mocking External Dependencies

  • Use Jest mock functions for JavaScript
  • Use pytest monkeypatch for Python
  • Create dedicated mock services for complex dependencies

Code Coverage

  • Track code coverage with Jest/Istanbul for JavaScript
  • Track code coverage with pytest-cov for Python
  • Generate coverage reports in CI/CD pipelines

Testing ML Components

Testing machine learning components requires special approaches:

Model Testing

  • Validate model outputs against ground truth
  • Test edge cases and potential biases
  • Measure performance metrics (accuracy, F1 score, etc.)

Data Pipeline Testing

  • Verify data preprocessing steps
  • Test data transformations
  • Validate training data quality

Continuous Testing

Automated testing is integrated into the development workflow:

  • Pre-commit hooks run linting and unit tests
  • CI/CD pipelines run all test levels
  • Staging deployments include integration and e2e tests

Testing Tools and Resources

  • Jest: JavaScript testing framework
  • pytest: Python testing framework
  • supertest: HTTP assertion library
  • Cypress: End-to-end testing framework
  • k6: Performance testing tool

Documentation

  • Each project's README.md includes testing instructions
  • Test directories contain README files with specific guidance
  • CI/CD configuration documents testing steps