cognitive/docs/guides/validation.md

title	type	status	created	tags	semantic_relations
Validation Framework	guide	draft	2024-02-12	validation quality testing	type links implements ai_validation_framework type links relates unit_testing quality_metrics

Validation Framework

Overview

This guide outlines the validation framework used to ensure theoretical consistency, implementation correctness, and quality standards across the cognitive modeling system.

Validation Layers

1. Theoretical Validation

• Mathematical consistency
• Theoretical soundness
• Formal proofs
• Constraint satisfaction

2. Implementation Validation

• Code correctness
• Algorithm implementation
• Numerical stability
• Performance optimization

3. Empirical Validation

• Experimental results
• Benchmark comparisons
• Real-world testing
• Performance metrics

Validation Methods

Mathematical Validation

def validate_probability_distribution(distribution):
    """Validate probability distribution properties."""
    # Check normalization
    assert np.isclose(distribution.sum(), 1.0)
    
    # Check non-negativity
    assert np.all(distribution >= 0)
    
    # Check numerical stability
    assert np.all(np.isfinite(distribution))

Implementation Validation

def validate_model_implementation(model):
    """Validate model implementation."""
    # Check interface compliance
    validate_interface(model)
    
    # Verify state consistency
    validate_state(model)
    
    # Test core functionality
    validate_behavior(model)

Empirical Validation

def validate_model_performance(model, benchmark_data):
    """Validate model performance."""
    # Run benchmark tests
    results = run_benchmarks(model, benchmark_data)
    
    # Compare against baselines
    validate_metrics(results, benchmarks)
    
    # Check performance criteria
    validate_performance(results)

Validation Workflow

1. Pre-implementation

• Review theoretical foundations
• Verify mathematical proofs
• Check assumptions
• Plan validation tests

2. During Implementation

• Unit testing
• Integration testing
• Property testing
• Performance profiling

3. Post-implementation

• System validation
• Benchmark testing
• Documentation review
• Code review

Validation Tools

Static Analysis

• Type checking
• Code linting
• Complexity analysis
• Dependency validation

Dynamic Analysis

• Runtime monitoring
• Memory profiling
• Performance tracking
• Coverage analysis

Quality Metrics

• Code quality
• Test coverage
• Documentation completeness
• Performance benchmarks

Best Practices

Documentation

• Document assumptions
• Specify constraints
• Detail validation methods
• Record test cases

Testing

• Comprehensive test suite
• Edge case coverage
• Performance benchmarks
• Integration tests

Review Process

• Code review
• Theory review
• Documentation review
• Performance review

Validation Checklist

Theory

• Mathematical consistency
• Theoretical soundness
• Constraint satisfaction
• Edge case handling

Implementation

• Code correctness
• Algorithm accuracy
• Numerical stability
• Performance optimization

Testing

• Unit tests
• Integration tests
• Benchmark tests
• Performance tests

Related Documentation

• unit_testing
• quality_metrics
• ai_validation_framework