kkahol-toronto/githubcopilot-mcp-coding-agent

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  "version": "1.0.0",
  "tool": "codedelite.log_response",
  "type": "response",
  "original_query": "original user query",
  "response": "AI generated response",
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  "query": "what is pytest. give me a getting started guide.",
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{
  "ts": "2025-09-04T17:53:54.605861+00:00",
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  "original_query": "what is pytest. give me a getting started guide.",
  "response": "# Pytest Getting Started Guide\n\n## Plan\nI've provided a comprehensive pytest guide covering:\n1. **What pytest is** and its key advantages\n2. **Installation and setup** process\n3. **Core testing concepts** with practical examples\n4. **Advanced features** like fixtures and parametrization\n5. **Best practices** and testing patterns\n\n## What is Pytest?\n\n**Pytest** is a mature, feature-rich Python testing framework that makes it easy to write simple and scalable test cases. It's designed to support simple unit testing as well as complex functional testing for applications and libraries.\n\n### Key Advantages:\n- **Simple syntax**: Write tests with plain `assert` statements\n- **Auto-discovery**: Automatically finds and runs test files\n- **Rich plugin ecosystem**: Extensive third-party plugins\n- **Detailed failure reporting**: Clear, informative test failure messages\n- **Fixtures**: Powerful dependency injection system\n- **Parametrization**: Run tests with multiple input sets\n\n## Complete Implementation\n\nI've created a comprehensive pytest demonstration project with:\n\n### Core Files Created:\n1. **`pytest_demo/calculator.py`** - A feature-rich calculator class with proper error handling and documentation\n2. **`pytest_demo/test_calculator.py`** - Comprehensive test suite demonstrating all pytest features\n3. **`pytest_demo/test_advanced.py`** - Advanced testing patterns including mocking, fixtures, and performance testing\n4. **`pytest_demo/conftest.py`** - Shared fixtures and pytest configuration\n5. **`pyproject.toml`** - Project configuration with pytest settings\n6. **`PYTEST_GUIDE.md`** - Complete documentation and tutorial\n7. **`run_tests.py`** - Test runner script with different execution options\n\n### Key Features Demonstrated:\n\n#### 1. **Basic Testing Patterns**\n```python\ndef test_add_positive_numbers(calculator):\n    result = calculator.add(5, 3)\n    assert result == 8\n```\n\n#### 2. **Exception Testing**\n```python\ndef test_divide_by_zero_raises_value_error(calculator):\n    with pytest.raises(ValueError, match=\"Cannot divide by zero\"):\n        calculator.divide(10, 0)\n```\n\n#### 3. **Parametrized Tests**\n```python\n@pytest.mark.parametrize(\"a, b, expected\", [\n    (1, 2, 3),\n    (0, 0, 0),\n    (-1, 1, 0),\n])\ndef test_add_parametrized(calculator, a, b, expected):\n    result = calculator.add(a, b)\n    assert result == expected\n```\n\n#### 4. **Fixtures for Dependency Injection**\n```python\n@pytest.fixture\ndef calculator():\n    return Calculator()\n\n@pytest.fixture(scope=\"session\")\ndef temp_dir():\n    with tempfile.TemporaryDirectory() as tmp_dir:\n        yield Path(tmp_dir)\n```\n\n#### 5. **Test Markers and Categories**\n```python\n@pytest.mark.slow\ndef test_large_factorial():\n    result = factorial(20)\n    assert result == 2432902008176640000\n\n@pytest.mark.integration\ndef test_complex_calculation_chain(calculator):\n    # Test multiple operations together\n```\n\n### Live Demo Results:\n\nI executed the tests to show pytest in action:\n\n**✅ 56 tests passed** with detailed output showing:\n- Test discovery and execution\n- Clear pass/fail indicators\n- Coverage analysis (97% for calculator.py)\n- Parametrized test execution\n- Error reporting for intentional failures\n\n### Command Examples:\n\n```bash\n# Run all tests\npytest\n\n# Run with verbose output\npytest -v\n\n# Run specific test file\npytest test_calculator.py\n\n# Run tests matching pattern\npytest -k \"add\"\n\n# Run with coverage\npytest --cov=src --cov-report=html\n\n# Skip slow tests\npytest -m \"not slow\"\n\n# Run only unit tests\npytest -m \"unit\"\n```\n\n## Design Choices & Trade-offs\n\n1. **Comprehensive Calculator Example**: Chose a calculator as it's familiar yet complex enough to demonstrate all pytest features\n2. **Multiple Test Categories**: Organized tests into logical groups (basic, exceptions, parametrized, etc.)\n3. **Fixture Scoping**: Demonstrated different fixture scopes for optimal performance\n4. **Configuration**: Used pyproject.toml for modern Python project standards\n5. **Error Handling**: Included comprehensive exception testing\n\n## Unit Tests Included\n\nThe project includes extensive unit tests covering:\n- ✅ **56 test functions** across multiple test classes\n- ✅ **Basic operations** (add, subtract, multiply, divide)\n- ✅ **Edge cases** (infinity, NaN, very large numbers)\n- ✅ **Exception handling** with proper error messages\n- ✅ **Parametrized tests** with multiple input sets\n- ✅ **Fixture usage** for setup and teardown\n- ✅ **Mocking examples** for external dependencies\n- ✅ **History tracking** and state management\n- ✅ **Performance considerations** for slow operations\n\n### Running Instructions:\n```bash\n# Install dependencies\npip install pytest pytest-cov pytest-mock\n\n# Run basic tests\npython run_tests.py --basic\n\n# Run all tests with coverage\npython run_tests.py --all\n\n# Generate HTML reports\npython run_tests.py --reports\n```\n\n## Alternative Implementations\n\n### Alternative 1: Simpler Structure\n**Pros**: Easier to understand for beginners\n**Cons**: Less comprehensive, fewer advanced features\n\n```python\n# Simple approach\ndef test_addition():\n    assert add(2, 3) == 5\n\ndef test_division():\n    assert divide(10, 2) == 5\n```\n\n### Alternative 2: Class-based Testing (unittest style)\n**Pros**: Familiar to developers from other frameworks\n**Cons**: More verbose, less pythonic\n\n```python\nclass CalculatorTest(unittest.TestCase):\n    def setUp(self):\n        self.calc = Calculator()\n    \n    def test_addition(self):\n        self.assertEqual(self.calc.add(2, 3), 5)\n```\n\n### Alternative 3: Property-based Testing (Hypothesis)\n**Pros**: Tests more edge cases automatically\n**Cons**: Requires additional dependency, more complex\n\n```python\nfrom hypothesis import given, strategies as st\n\n@given(st.integers(), st.integers())\ndef test_addition_commutative(a, b):\n    assert add(a, b) == add(b, a)\n```\n\n## Next Steps\n\n1. **Explore Plugins**: pytest-django, pytest-asyncio, pytest-benchmark\n2. **CI/CD Integration**: Set up automated testing in GitHub Actions\n3. **Property-based Testing**: Add Hypothesis for more thorough testing\n4. **Performance Testing**: Use pytest-benchmark for performance regression testing\n5. **Test Coverage Goals**: Aim for >90% coverage with meaningful tests\n\n## Assumptions Made\n\n- Python 3.8+ environment\n- Familiarity with basic Python concepts\n- Interest in comprehensive testing practices\n- VS Code as development environment\n- Modern project structure preferences\n\nThe implementation demonstrates pytest's power while maintaining readability and educational value. Each test serves as both validation and documentation of expected behavior.",
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{
  "query": "string - The user's coding question"
}

{
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  "suggested_copilot_message": "string - Complete message for AI client",
  "meta": {
    "version": "string",
    "server": "string", 
    "timestamp": "number",
    "session_id": "string - UUID for linking responses"
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{
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  "response": "string - AI generated response",
  "session_id": "string - UUID from generate tool",
  "response_metadata": {
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HOUSE_BLOCK = """\
Please adhere to all the following standards and behaviors:

1) Code Quality & Linting
   - Follow idiomatic patterns and established style guides
   - Include docstrings/comments for complex logic
   - Keep functions cohesive; avoid excessive side effects

2) Unit Tests
   - Provide unit tests for all non-trivial functions
   - Use realistic fixtures/mocks; cover edge cases
   - Show clear instructions for running tests

# Add your custom standards here...
"""

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  "user_feedback": "helpful",
  "code_quality_score": 9.5
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import logging
logging.basicConfig(level=logging.DEBUG)