Skip to main content

Project-Wide Testing Strategy

This document outlines the comprehensive testing strategy for the Codomyrmex project, ensuring code quality, reliability, and maintainability across all modules.

1. Overview​

Our testing strategy incorporates multiple levels of testing to cover different aspects of the software:

  • Unit Tests: Focus on individual components (functions, classes, methods) in isolation.
  • Integration Tests: Verify the interaction between different components or modules.
  • End-to-End (E2E) Tests: Validate complete workflows and user scenarios from an external perspective.
  • Static Analysis: Automated checks for code style, potential bugs, and security vulnerabilities without executing the code.

2. Testing Levels​

2.1. Unit Tests​

  • Goal: To verify that each unit of the software performs as designed.
  • Scope: Individual functions, methods, or classes.
  • Tools: pytest for test execution, pytest-mock for mocking dependencies.
  • Location: Within each module's tests/unit/ directory (e.g., ai_code_editing/tests/unit/).
  • Frequency: Run frequently during development and as part of pre-commit/pre-push hooks and CI pipelines.
  • Coverage: Aim for high unit test coverage (e.g., >80%) for critical logic.
  • Characteristics:
    • Fast to execute.
    • Independent of other tests.
    • Deterministic results.
    • Focus on a small piece of code.

2.2. Integration Tests​

  • Goal: To expose defects in the interfaces and interactions between integrated components or modules.
  • Scope: Interaction between two or more components, services, or modules. This includes testing interactions with external systems like databases or APIs where feasible (using mocks or dedicated test instances).
  • Tools: pytest, potentially with helper libraries for setting up test environments or data.
  • Location: Within each module's tests/integration/ directory (e.g., ai_code_editing/tests/integration/). Cross-module integration tests can reside in a top-level tests/integration/ directory if necessary.
  • Frequency: Run as part of CI pipelines after unit tests pass.
  • Characteristics:
    • Slower than unit tests.
    • May require more complex setup (e.g., test databases, mock services).
    • Focus on data flow and control flow between components.

2.3. End-to-End (E2E) Tests​

  • Goal: To validate the entire application flow from the user's perspective, simulating real user scenarios.
  • Scope: Complete application workflows, involving multiple modules and potentially external systems.
  • Tools: To be determined based on application type (e.g., Selenium for web UIs, custom scripts for API-driven systems). For Codomyrmex, this will likely involve testing sequences of MCP tool calls and verifying outputs.
  • Location: A dedicated top-level tests/e2e/ directory.
  • Frequency: Run less frequently than unit/integration tests, typically on a schedule (e.g., nightly builds) or before major releases.
  • Characteristics:
    • Slowest type of test.
    • Most complex to write and maintain.
    • Provide the highest confidence in the overall system health.
    • Can be brittle if not designed carefully.

2.4. Static Analysis​

  • Goal: To identify potential issues, enforce coding standards, and improve code quality without executing the code.
  • Scope: Entire codebase.
  • Tools:
    • pylint for general Python linting.
    • ruff for style, import ordering, and formatting enforcement.
    • bandit for security vulnerability scanning.
    • radon for code complexity metrics.
    • lizard for cyclomatic complexity and token count analysis.
    • The repository type checker ty.
  • Location: Configuration files (e.g., pyproject.toml, .pylintrc) and integrated into pre-commit hooks and CI pipelines.
  • Frequency: Run on every commit (via hooks) and in CI pipelines.

3. Test Data Management​

  • Use realistic but anonymized or synthetic data for testing.
  • Avoid using production data in test environments.
  • Test data should be version-controlled or generated on-the-fly where appropriate.
  • Ensure test data covers a wide range of scenarios, including edge cases and invalid inputs.

4. Test Execution and Reporting​

  • CI/CD Integration: All tests (unit, integration, static analysis) will be integrated into the CI/CD pipeline (e.g., GitHub Actions).
  • Test Reports: CI pipeline will generate test reports, including coverage reports (pytest-cov).
  • Failure Policy: Build failures will occur if any critical tests fail or if coverage drops below a defined threshold.

5. Roles and Responsibilities​

  • Developers: Responsible for writing unit and integration tests for the code they develop. Also responsible for ensuring their code passes static analysis checks.
  • QA/Test Engineers (if applicable): Responsible for designing and implementing E2E tests and managing overall test strategy.
  • All Contributors: Responsible for running relevant tests locally before pushing code.

6. Continuous Improvement​

The testing strategy will be reviewed and updated periodically based on project needs, feedback, and evolving best practices. Regular analysis of test failures and escaped defects will be used to identify areas for improvement in the testing process.

7. Module-Specific Testing Considerations​

Each module should have a tests/README.md file detailing:

  • Specific setup instructions for running its tests.
  • Key test scenarios covered.
  • Any module-specific testing tools or libraries used.
  • Guidance on how to add new tests for the module.

This project-wide strategy provides a baseline, and modules may extend it with more specific approaches as needed, documenting these in their respective tests/README.md files.