cognitive/docs/guides/unit_testing.md

title	type	status	created	tags	semantic_relations
Unit Testing Guide	guide	draft	2024-02-12	testing development quality	type links implements ai_validation_framework type links relates implementation_guides model_implementation

Unit Testing Guide

Overview

This guide provides comprehensive instructions for unit testing in the cognitive modeling framework. It covers best practices, testing patterns, and validation approaches.

Testing Philosophy

Core Principles

• Test behavior, not implementation
• One assertion per test
• Keep tests simple and readable
• Test edge cases and error conditions
• Maintain test independence

Test Types

1. Unit Tests

   • Individual component testing
   • Function-level validation
   • Class behavior verification

2. Integration Tests

   • Component interaction testing
   • System integration validation
   • End-to-end workflows

3. Property Tests

   • Invariant verification
   • Property-based testing
   • Randomized input testing

Testing Structure

Directory Organization

tests/
├── unit/              # Unit tests
├── integration/       # Integration tests
├── property/         # Property-based tests
└── fixtures/         # Test data and fixtures

File Naming

• Test files: test_*.py
• Test classes: Test*
• Test methods: test_*

Writing Tests

Basic Test Structure

def test_function_name():
    # Arrange
    input_data = prepare_test_data()
    
    # Act
    result = function_under_test(input_data)
    
    # Assert
    assert result == expected_output

Test Fixtures

@pytest.fixture
def sample_model():
    """Create a sample model for testing."""
    return Model(params)

Parameterized Tests

@pytest.mark.parametrize("input,expected", [
    (value1, result1),
    (value2, result2)
])
def test_parameterized(input, expected):
    assert function(input) == expected

Testing Patterns

Model Testing

• Test model initialization
• Verify state transitions
• Validate output distributions
• Check error handling

Algorithm Testing

• Test convergence properties
• Verify numerical stability
• Check optimization behavior
• Validate against known solutions

Data Structure Testing

• Test data integrity
• Verify structure constraints
• Check serialization
• Validate transformations

Best Practices

Code Coverage

• Aim for high test coverage
• Focus on critical paths
• Test edge cases
• Cover error conditions

Test Maintenance

• Keep tests up to date
• Refactor when needed
• Document test purpose
• Review test quality

Performance

• Use appropriate fixtures
• Minimize test duration
• Profile slow tests
• Optimize test data

Tools and Libraries

Testing Framework

• pytest for test execution
• pytest-cov for coverage
• pytest-benchmark for performance
• pytest-mock for mocking

Assertion Libraries

• pytest assertions
• numpy.testing
• pandas.testing
• torch.testing

Mocking

• unittest.mock
• pytest-mock
• responses for HTTP
• moto for AWS

Continuous Integration

CI Pipeline

1. Run unit tests
2. Check coverage
3. Run integration tests
4. Generate reports

Quality Gates

• Minimum coverage: 80%
• All tests must pass
• No critical issues
• Performance thresholds

Related Documentation

• ai_validation_framework
• implementation_guides
• model_implementation