cognitive/docs/guides/unit_testing.md

---
title: Unit Testing Guide
type: guide
status: draft
created: 2024-02-12
tags:
  - testing
  - development
  - quality
semantic_relations:
  - type: implements
    links: [[ai_validation_framework]]
  - type: relates
    links:
      - [[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
```python
def test_function_name():
    # Arrange
    input_data = prepare_test_data()
    
    # Act
    result = function_under_test(input_data)
    
    # Assert
    assert result == expected_output
```

### Test Fixtures
```python
@pytest.fixture
def sample_model():
    """Create a sample model for testing."""
    return Model(params)
```

### Parameterized Tests
```python
@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]]