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Developer Documentation for PANTHER

This guide provides technical details for contributors and maintainers of PANTHER. Learn about the codebase, design principles, and guidelines for extending the system.

Code Architecture

Overview

PANTHER is built with modularity and extensibility in mind. Key directories:

  • core/: Contains core logic for experiment execution and management.
  • config/: Handles experiment configurations and schema validation.
  • plugins/: Houses plugins for protocols, services, and environments.
  • outputs/: Stores logs and results.

Design Principles

  1. Modularity:

    • Each component has a specific responsibility, making the system easier to extend and maintain.

  2. Dynamic Loading:

    • Plugins and configurations are dynamically loaded to support extensibility.

  3. Error Handling:

    • Comprehensive logging and exception management ensure robustness.

Key Components

ExperimentManager

  • Orchestrates the execution of experiments.
  • Responsible for initializing test cases, deploying services, and managing results.

TestCase

  • Represents individual tests within an experiment.
  • Encapsulates service deployment, step execution, and assertion validation.

PluginLoader

  • Dynamically loads and validates plugins (e.g., protocols, environments).
  • Handles Docker integration for plugins requiring containerized environments.

Adding New Features

  1. Define Requirements:

    • Clearly define the feature's purpose and scope.

  2. Modify Core Components (if necessary):

    • Update ExperimentManager or TestCase for new test types.

  3. Develop Plugins:

    • Create new plugins in plugins/ if extending protocols or services.

  4. Update Configuration Schema:

    • Modify config_schema.py to support new configuration options.

  5. Write Unit Tests:

    • Add tests in tests/ to ensure feature reliability.

Refactoring Guidelines

  1. Follow DRY:

    • Avoid duplicating code; extract common functionality into reusable functions.

  2. Preserve Backward Compatibility:

    • Ensure existing experiments and configurations remain functional.

  3. Write Comprehensive Tests:

    • Validate changes using unit and integration tests.

Error Handling

Logging

  • All components use Python’s logging module for structured logging.
  • Logs are saved in outputs/logs for debugging.

Exceptions

  • Raise meaningful exceptions with descriptive messages.
  • Catch and log exceptions in critical areas (e.g., ExperimentManager).

Contributing

  1. Fork the repository and create a new branch for your feature or bug fix.
  2. Run the tests to ensure stability: 
    pytest tests/
  3. Submit a pull request with detailed explanations of your changes.

For additional guidance, consult the Main README or contact the development team.