Python Code Tester

Project Description

The Python Code Tester is a versatile tool designed for testing, analyzing, and evaluating Python code through a streamlined interface. It allows users to run code, view metrics, log errors, and customize settings for various testing scenarios, making it ideal for both beginners and advanced developers.

Features

• User-Friendly Interface: Simplified UI through ui.py, providing clear options for running tests and viewing results.
• Code Analysis: Detailed analysis with analysis.py to evaluate code performance and accuracy metrics.
• Configurable Testing: Modify settings.json to customize test parameters and behavior.
• Metrics Tracking: metrics.json logs test results and performance data for consistent tracking.
• Comprehensive Logging: Track application events and errors with app.log, assisting in debugging.

Installation

1. Clone the repository:

   git clone <repository-url>

2. Navigate to the project directory:

   cd Python-Code-Tester

3. Install dependencies: Ensure you have Python 3.6+ installed, then run:

   pip install -r requirements.txt

File Descriptions and Functions

1. main.py

• Purpose: The main entry point to run the application.
• Functions:
  • run_tests(): Initializes testing routines and loads settings from settings.json.
  • generate_report(): Compiles test results into metrics.json for post-test review.
• Usage: Run main.py to start the application, execute tests, and view results.

2. ui.py

• Purpose: Provides the graphical user interface (GUI) for user interaction.
• Functions:
  • initialize_ui(): Sets up the initial layout, buttons, and inputs for the application interface.
  • display_results(): Shows real-time test results and feedback to the user.
• Usage: ui.py is called within main.py, so no separate action is needed. Use the GUI to initiate tests and view outputs.

3. analysis.py

• Purpose: Analyzes code performance and calculates relevant metrics.
• Functions:
  • evaluate_code_quality(): Runs quality checks on the tested code, assessing aspects like efficiency and adherence to coding standards.
  • calculate_performance_metrics(): Measures and logs performance data (e.g., execution time).
• Usage: analysis.py is invoked by main.py during testing, so results appear automatically in the metrics.

4. settings.json

• Purpose: Stores configurations for the code tester (e.g., test parameters, code quality thresholds).
• Structure:

     {
     "selected_tools": [
         "Pylint",
         "Mypy",
         "Radon",
         "Bandit",
         "Black",
         "Pydocstyle",
         "Pip-Licenses",
         "Pytest",
         "Pip-Audit",
         "Autopep8",
         "Isort",
         "Flake8"
     ],
     "pylint_fail_under": 5,
     "radon_threshold": 10,
     "bandit_confidence": "MEDIUM",
     "report_format": "PDF",
     "enable_git_integration": false,
     "enable_autofix": false,
     "enable_metrics_tracking": false
     }

• Usage: Edit settings.json to modify testing parameters. Adjust values as needed, and main.py will read these on startup.

5. metrics.json

• Purpose: Logs testing results and metrics for each session.
• Sample Structure:

  {
    "execution_time": 1.8,
    "memory_usage": 485,
    "code_quality": 95
  }

• Usage: View metrics.json after running tests to examine performance and quality scores.

6. app.log

• Purpose: Logs events, errors, and warnings, helping to debug and monitor the app.
• Usage: Check app.log for any runtime issues or detailed logs of application events.

Step-by-Step Usage Tutorial

1. Prepare Your Settings: Open settings.json and customize testing parameters as needed. For example, adjust "test_iterations" to control the number of test repetitions.
2. Run the Application:

   python main.py

3. Interact with the GUI:
   • Start Tests: In the UI, click the "Start Test" button to begin testing your code based on the configurations.
   • View Real-Time Results: As the code executes, results are displayed in real-time.
4. Analyze Results:
   • Open metrics.json to review execution time, memory usage, and code quality metrics.
   • Use this data to make necessary optimizations to your code.
5. Debugging: If any issues occur, refer to app.log for detailed error and event logs.

Project Gallery

Here are some images showcasing the Python Code Tester interface and functionality.

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Contributing

Contributions are welcome! Please submit a pull request or open an issue to suggest changes or improvements.

Contact

For questions, please reach out to the project maintainer at samuellabant@gmail.com.

License

This project is licensed under the MIT License. See the LICENSE file for details.