diff --git a/modules/source/00_setup/setup_dev.py b/modules/source/00_setup/setup_dev.py
index 0c0190b0..3d02245a 100644
--- a/modules/source/00_setup/setup_dev.py
+++ b/modules/source/00_setup/setup_dev.py
@@ -518,13 +518,115 @@ assert sys_info['python_version'] == actual_version, "Python version should matc
 
 print("✅ System info function tests passed!")
 print(f"✅ Python: {sys_info['python_version']} on {sys_info['platform']}")
-print(f"✅ Hardware: {sys_info['cpu_count']} cores, {sys_info['memory_gb']} GB RAM")
+print(f"✅ Memory: {sys_info['memory_gb']} GB, CPUs: {sys_info['cpu_count']}")
 
 # %% [markdown]
 """
-## 🎯 Module Summary: Foundation of ML Systems Engineering
+### 🧪 Inline Test Functions
 
-Congratulations! You've successfully configured your TinyTorch installation and learned the foundations of ML systems engineering:
+These test functions provide immediate feedback when developing your solutions:
+"""
+
+# %%
+def test_personal_info():
+    """Test personal_info function implementation."""
+    print("🔬 Unit Test: Personal Information...")
+    
+    # Test personal_info function
+    personal = personal_info()
+    
+    # Test return type
+    assert isinstance(personal, dict), "personal_info should return a dictionary"
+    
+    # Test required keys
+    required_keys = ['developer', 'email', 'institution', 'system_name', 'version']
+    for key in required_keys:
+        assert key in personal, f"Dictionary should have '{key}' key"
+    
+    # Test non-empty values
+    for key, value in personal.items():
+        assert isinstance(value, str), f"Value for '{key}' should be a string"
+        assert len(value) > 0, f"Value for '{key}' cannot be empty"
+    
+    # Test email format
+    assert '@' in personal['email'], "Email should contain @ symbol"
+    assert '.' in personal['email'], "Email should contain domain"
+    
+    # Test version format
+    assert personal['version'] == '1.0.0', "Version should be '1.0.0'"
+    
+    # Test system name (should be unique/personalized)
+    assert len(personal['system_name']) > 5, "System name should be descriptive"
+    
+    print("✅ Personal info function tests passed!")
+    print(f"✅ TinyTorch configured for: {personal['developer']}")
+
+# %%
+def test_system_info():
+    """Test system_info function implementation."""
+    print("🔬 Unit Test: System Information...")
+    
+    # Test system_info function
+    sys_info = system_info()
+    
+    # Test return type
+    assert isinstance(sys_info, dict), "system_info should return a dictionary"
+    
+    # Test required keys
+    required_keys = ['python_version', 'platform', 'architecture', 'cpu_count', 'memory_gb']
+    for key in required_keys:
+        assert key in sys_info, f"Dictionary should have '{key}' key"
+    
+    # Test data types
+    assert isinstance(sys_info['python_version'], str), "python_version should be string"
+    assert isinstance(sys_info['platform'], str), "platform should be string"
+    assert isinstance(sys_info['architecture'], str), "architecture should be string"
+    assert isinstance(sys_info['cpu_count'], int), "cpu_count should be integer"
+    assert isinstance(sys_info['memory_gb'], (int, float)), "memory_gb should be number"
+    
+    # Test reasonable values
+    assert sys_info['cpu_count'] > 0, "CPU count should be positive"
+    assert sys_info['memory_gb'] > 0, "Memory should be positive"
+    assert len(sys_info['python_version']) > 0, "Python version should not be empty"
+    
+    # Test that values are actually queried (not hardcoded)
+    actual_version = f"{sys.version_info.major}.{sys.version_info.minor}.{sys.version_info.micro}"
+    assert sys_info['python_version'] == actual_version, "Python version should match actual system"
+    
+    print("✅ System info function tests passed!")
+    print(f"✅ Python: {sys_info['python_version']} on {sys_info['platform']}")
+
+# %%
+def test_integration():
+    """Test integration of personal_info and system_info functions."""
+    print("🔬 Integration Test: Setup Functions...")
+    
+    # Test that both functions work together
+    personal = personal_info()
+    sys_info = system_info()
+    
+    # Test that they return different types of information
+    assert 'developer' in personal, "Personal info should contain developer"
+    assert 'python_version' in sys_info, "System info should contain python_version"
+    
+    # Test that system name is personalized (not default)
+    assert personal['system_name'] != 'TinyTorch-System', "System name should be personalized"
+    assert personal['developer'] != 'Your Name', "Developer name should be filled in"
+    assert personal['email'] != 'your.email@example.com', "Email should be filled in"
+    
+    print("✅ Integration test passed!")
+    print("✅ Setup module fully configured and ready!")
+
+# Run the inline tests
+test_personal_info()
+test_system_info() 
+test_integration()
+
+# %% [markdown]
+"""
+## 🎯 Professional ML Engineering Skills
+
+You've successfully configured your TinyTorch installation and learned the foundations of ML systems engineering:
 
 ### What You've Accomplished
 ✅ **Personal Configuration**: Set up your identity and custom system name  
@@ -605,4 +707,668 @@ You've taken your first step in ML systems engineering! This module taught you:
 - **Foundation building**: Creating reusable, tested, documented code
 
 **Ready for the next challenge?** Let's build the foundation of ML systems with tensors!
-""" 
\ No newline at end of file
+""" 
+
+# %% [markdown]
+"""
+## Step 4: Environment Validation
+
+### The Concept: Dependency Management in ML Systems
+**Environment validation** ensures your system has the necessary packages and versions for ML development. This is crucial because ML systems have complex dependency chains that can break in subtle ways.
+
+### Why Environment Validation Matters
+
+#### 1. **Compatibility Assurance**
+- **Version conflicts**: Different packages may require incompatible versions
+- **API changes**: New versions might break existing code
+- **Feature availability**: Some features require specific versions
+
+#### 2. **Reproducibility**
+- **Environment documentation**: Exact package versions for reproduction
+- **Dependency tracking**: Understanding what's installed and why
+- **Debugging support**: Version info helps troubleshoot issues
+
+#### 3. **Professional Development**
+- **Deployment safety**: Ensure development matches production
+- **Collaboration**: Team members need compatible environments
+- **Quality assurance**: Validate setup before beginning work
+
+### Essential ML Dependencies
+We'll check for core packages that ML systems depend on:
+- **numpy**: Fundamental numerical computing
+- **matplotlib**: Visualization and plotting
+- **psutil**: System information and monitoring
+- **jupyter**: Interactive development environment
+- **nbdev**: Package development tools
+- **pytest**: Testing framework
+
+### Real-World Applications
+- **Docker**: Container images include dependency validation
+- **CI/CD**: Automated testing validates environment setup
+- **MLflow**: Tracks package versions with experiment metadata
+- **Kaggle**: Validates package availability in competition environments
+
+Let's implement environment validation!
+"""
+
+# %% nbgrader={"grade": false, "grade_id": "environment-validation", "locked": false, "schema_version": 3, "solution": true, "task": false}
+#| export
+import importlib
+import pkg_resources
+from typing import Dict, List, Optional
+
+def validate_environment() -> Dict[str, Any]:
+    """
+    Validate ML development environment and check essential dependencies.
+    
+    This function checks that your system has the necessary packages for ML development.
+    It's like a pre-flight check before you start building ML systems.
+    
+    TODO: Implement environment validation.
+    
+    STEP-BY-STEP IMPLEMENTATION:
+    1. Define list of essential ML packages to check
+    2. For each package, try to import it and get version
+    3. Track which packages are available vs missing
+    4. Calculate environment health score
+    5. Return comprehensive environment report
+    
+    ESSENTIAL PACKAGES TO CHECK:
+    - numpy: Numerical computing foundation
+    - matplotlib: Visualization and plotting
+    - psutil: System monitoring
+    - jupyter: Interactive development
+    - nbdev: Package development
+    - pytest: Testing framework
+    
+    IMPLEMENTATION HINTS:
+    - Use try/except to handle missing packages gracefully
+    - Use pkg_resources.get_distribution(package).version for versions
+    - Calculate health_score as (available_packages / total_packages) * 100
+    - Round health_score to 1 decimal place
+    """
+    ### BEGIN SOLUTION
+    essential_packages = [
+        'numpy', 'matplotlib', 'psutil', 'jupyter', 'nbdev', 'pytest'
+    ]
+    
+    available = {}
+    missing = []
+    
+    for package in essential_packages:
+        try:
+            # Try to import the package
+            importlib.import_module(package)
+            # Get version information
+            version = pkg_resources.get_distribution(package).version
+            available[package] = version
+        except (ImportError, pkg_resources.DistributionNotFound):
+            missing.append(package)
+    
+    # Calculate health score
+    total_packages = len(essential_packages)
+    available_packages = len(available)
+    health_score = round((available_packages / total_packages) * 100, 1)
+    
+    return {
+        'available_packages': available,
+        'missing_packages': missing,
+        'health_score': health_score,
+        'total_checked': total_packages,
+        'status': 'healthy' if health_score >= 80 else 'needs_attention'
+    }
+    ### END SOLUTION
+
+# %% [markdown]
+"""
+## Step 5: Performance Benchmarking
+
+### The Concept: Hardware Performance Profiling
+**Performance benchmarking** measures your system's computational capabilities for ML workloads. This helps you understand your hardware limits and optimize your development workflow.
+
+### Why Performance Benchmarking Matters
+
+#### 1. **Resource Planning**
+- **Training time estimation**: How long will model training take?
+- **Memory allocation**: What's the maximum batch size you can handle?
+- **Parallelization**: How many cores can you effectively use?
+
+#### 2. **Optimization Guidance**
+- **Bottleneck identification**: Is your system CPU-bound or memory-bound?
+- **Hardware upgrades**: What would improve performance most?
+- **Algorithm selection**: Which algorithms suit your hardware?
+
+#### 3. **Performance Comparison**
+- **Baseline establishment**: Track performance over time
+- **System comparison**: Compare different development environments
+- **Deployment planning**: Match development to production performance
+
+### Benchmarking Strategy
+We'll test key ML operations:
+- **CPU computation**: Matrix operations that stress the processor
+- **Memory bandwidth**: Large data transfers that test memory speed
+- **Overall system**: Combined CPU and memory performance
+
+### Real-World Applications
+- **MLPerf**: Industry-standard ML benchmarks
+- **Cloud providers**: Performance metrics for instance selection
+- **Hardware vendors**: Benchmark comparisons for purchasing decisions
+
+Let's implement performance benchmarking!
+"""
+
+# %% nbgrader={"grade": false, "grade_id": "performance-benchmark", "locked": false, "schema_version": 3, "solution": true, "task": false}
+#| export
+import time
+import random
+
+def benchmark_performance() -> Dict[str, Any]:
+    """
+    Benchmark system performance for ML workloads.
+    
+    This function measures computational performance to help you understand
+    your system's capabilities and optimize your ML development workflow.
+    
+    TODO: Implement performance benchmarking.
+    
+    STEP-BY-STEP IMPLEMENTATION:
+    1. CPU Test: Time a computationally intensive operation
+    2. Memory Test: Time a memory-intensive operation
+    3. Calculate performance scores based on execution time
+    4. Determine overall system performance rating
+    5. Return comprehensive benchmark results
+    
+    BENCHMARK TESTS:
+    - CPU: Nested loop calculation (computational intensity)
+    - Memory: Large list operations (memory bandwidth)
+    - Combined: Overall system performance score
+    
+    IMPLEMENTATION HINTS:
+    - Use time.time() to measure execution time
+    - CPU test: nested loops with mathematical operations
+    - Memory test: large list creation and manipulation
+    - Lower execution time = better performance
+    - Calculate scores as inverse of time (e.g., 1/time * 1000)
+    """
+    ### BEGIN SOLUTION
+    benchmarks = {}
+    
+    # CPU Performance Test
+    print("⚡ Running CPU benchmark...")
+    start_time = time.time()
+    
+    # CPU-intensive calculation
+    result = 0
+    for i in range(100000):
+        result += i * i + i / 2
+    
+    cpu_time = time.time() - start_time
+    benchmarks['cpu_time'] = round(cpu_time, 3)
+    benchmarks['cpu_score'] = round(1000 / cpu_time, 1)
+    
+    # Memory Performance Test
+    print("🧠 Running memory benchmark...")
+    start_time = time.time()
+    
+    # Memory-intensive operations
+    large_list = list(range(1000000))
+    large_list.reverse()
+    large_list.sort()
+    
+    memory_time = time.time() - start_time
+    benchmarks['memory_time'] = round(memory_time, 3)
+    benchmarks['memory_score'] = round(1000 / memory_time, 1)
+    
+    # Overall Performance Score
+    overall_score = round((benchmarks['cpu_score'] + benchmarks['memory_score']) / 2, 1)
+    benchmarks['overall_score'] = overall_score
+    
+    # Performance Rating
+    if overall_score >= 80:
+        rating = 'excellent'
+    elif overall_score >= 60:
+        rating = 'good'
+    elif overall_score >= 40:
+        rating = 'fair'
+    else:
+        rating = 'needs_optimization'
+    
+    benchmarks['performance_rating'] = rating
+    
+    return benchmarks
+    ### END SOLUTION
+
+# %% [markdown]
+"""
+## Step 6: Development Environment Setup
+
+### The Concept: Professional Development Configuration
+**Development environment setup** configures essential tools and settings for professional ML development. This includes Git configuration, Jupyter settings, and other tools that make development more efficient.
+
+### Why Development Setup Matters
+
+#### 1. **Professional Standards**
+- **Version control**: Proper Git configuration for collaboration
+- **Code quality**: Consistent formatting and style
+- **Documentation**: Automatic documentation generation
+
+#### 2. **Productivity Optimization**
+- **Tool configuration**: Optimized settings for efficiency
+- **Workflow automation**: Reduce repetitive tasks
+- **Error prevention**: Catch issues before they become problems
+
+#### 3. **Collaboration Readiness**
+- **Team compatibility**: Consistent development environment
+- **Code sharing**: Proper attribution and commit messages
+- **Project standards**: Follow established conventions
+
+### Essential Development Tools
+We'll configure key tools for ML development:
+- **Git**: Version control and collaboration
+- **Jupyter**: Interactive development environment
+- **Python**: Code formatting and quality tools
+
+Let's implement development environment setup!
+"""
+
+# %% nbgrader={"grade": false, "grade_id": "development-setup", "locked": false, "schema_version": 3, "solution": true, "task": false}
+#| export
+import subprocess
+import json
+from pathlib import Path
+
+def setup_development_environment() -> Dict[str, Any]:
+    """
+    Configure development environment for professional ML development.
+    
+    This function sets up essential tools and configurations to make your
+    development workflow more efficient and professional.
+    
+    TODO: Implement development environment setup.
+    
+    STEP-BY-STEP IMPLEMENTATION:
+    1. Check if Git is installed and configured
+    2. Verify Jupyter installation and configuration
+    3. Check Python development tools
+    4. Configure any missing tools
+    5. Return setup status and recommendations
+    
+    DEVELOPMENT TOOLS TO CHECK:
+    - Git: Version control system
+    - Jupyter: Interactive development
+    - Python tools: Code quality and formatting
+    
+    IMPLEMENTATION HINTS:
+    - Use subprocess.run() to check tool availability
+    - Use try/except to handle missing tools gracefully
+    - Provide helpful recommendations for missing tools
+    - Focus on tools that improve ML development workflow
+    """
+    ### BEGIN SOLUTION
+    setup_status = {}
+    recommendations = []
+    
+    # Check Git installation and configuration
+    try:
+        git_version = subprocess.run(['git', '--version'], 
+                                   capture_output=True, text=True, check=True)
+        setup_status['git_installed'] = True
+        setup_status['git_version'] = git_version.stdout.strip()
+        
+        # Check Git configuration
+        try:
+            git_name = subprocess.run(['git', 'config', 'user.name'], 
+                                    capture_output=True, text=True, check=True)
+            git_email = subprocess.run(['git', 'config', 'user.email'], 
+                                     capture_output=True, text=True, check=True)
+            setup_status['git_configured'] = True
+            setup_status['git_name'] = git_name.stdout.strip()
+            setup_status['git_email'] = git_email.stdout.strip()
+        except subprocess.CalledProcessError:
+            setup_status['git_configured'] = False
+            recommendations.append("Configure Git: git config --global user.name 'Your Name'")
+            recommendations.append("Configure Git: git config --global user.email 'your.email@domain.com'")
+    
+    except (subprocess.CalledProcessError, FileNotFoundError):
+        setup_status['git_installed'] = False
+        recommendations.append("Install Git: https://git-scm.com/downloads")
+    
+    # Check Jupyter installation
+    try:
+        jupyter_version = subprocess.run(['jupyter', '--version'], 
+                                       capture_output=True, text=True, check=True)
+        setup_status['jupyter_installed'] = True
+        setup_status['jupyter_version'] = jupyter_version.stdout.strip()
+    except (subprocess.CalledProcessError, FileNotFoundError):
+        setup_status['jupyter_installed'] = False
+        recommendations.append("Install Jupyter: pip install jupyter")
+    
+    # Check Python tools
+    python_tools = ['pip', 'python']
+    for tool in python_tools:
+        try:
+            tool_version = subprocess.run([tool, '--version'], 
+                                        capture_output=True, text=True, check=True)
+            setup_status[f'{tool}_installed'] = True
+            setup_status[f'{tool}_version'] = tool_version.stdout.strip()
+        except (subprocess.CalledProcessError, FileNotFoundError):
+            setup_status[f'{tool}_installed'] = False
+            recommendations.append(f"Install {tool}: Check Python installation")
+    
+    # Calculate setup health
+    total_tools = 4  # git, jupyter, pip, python
+    installed_tools = sum([
+        setup_status.get('git_installed', False),
+        setup_status.get('jupyter_installed', False),
+        setup_status.get('pip_installed', False),
+        setup_status.get('python_installed', False)
+    ])
+    
+    setup_health = round((installed_tools / total_tools) * 100, 1)
+    
+    return {
+        'setup_status': setup_status,
+        'recommendations': recommendations,
+        'setup_health': setup_health,
+        'status': 'ready' if setup_health >= 75 else 'needs_configuration'
+    }
+    ### END SOLUTION
+
+# %% [markdown]
+"""
+## Step 7: Comprehensive System Report
+
+### The Concept: Integrated System Analysis
+**Comprehensive system reporting** combines all your configuration and diagnostic information into a single, actionable report. This is like a "health check" for your ML development environment.
+
+### Why Comprehensive Reporting Matters
+
+#### 1. **Holistic View**
+- **Complete picture**: All system information in one place
+- **Dependency analysis**: How different components interact
+- **Performance context**: Understanding system capabilities
+
+#### 2. **Troubleshooting Support**
+- **Debugging aid**: Complete environment information for issue resolution
+- **Performance analysis**: Identify bottlenecks and optimization opportunities
+- **Compatibility checking**: Ensure all components work together
+
+#### 3. **Professional Documentation**
+- **Environment documentation**: Complete system specification
+- **Reproducibility**: All information needed to recreate environment
+- **Sharing**: Easy to share system information with collaborators
+
+Let's create a comprehensive system report!
+"""
+
+# %% nbgrader={"grade": false, "grade_id": "system-report", "locked": false, "schema_version": 3, "solution": true, "task": false}
+#| export
+from datetime import datetime
+
+def generate_system_report() -> Dict[str, Any]:
+    """
+    Generate comprehensive system report for ML development.
+    
+    This function combines all configuration and diagnostic information
+    into a single, actionable report for your ML development environment.
+    
+    TODO: Implement comprehensive system reporting.
+    
+    STEP-BY-STEP IMPLEMENTATION:
+    1. Gather personal information
+    2. Collect system information
+    3. Validate environment
+    4. Run performance benchmarks
+    5. Check development setup
+    6. Generate overall health score
+    7. Create comprehensive report with recommendations
+    
+    REPORT SECTIONS:
+    - Personal configuration
+    - System specifications
+    - Environment validation
+    - Performance benchmarks
+    - Development setup
+    - Overall health assessment
+    - Recommendations for improvement
+    
+    IMPLEMENTATION HINTS:
+    - Call all previously implemented functions
+    - Combine results into comprehensive report
+    - Calculate overall health score from all components
+    - Provide actionable recommendations
+    """
+    ### BEGIN SOLUTION
+    print("📊 Generating comprehensive system report...")
+    
+    # Gather all information
+    personal = personal_info()
+    system = system_info()
+    environment = validate_environment()
+    performance = benchmark_performance()
+    development = setup_development_environment()
+    
+    # Calculate overall health score (normalize performance score to 0-100 range)
+    normalized_performance = min(performance['overall_score'], 100)  # Cap at 100
+    
+    health_components = [
+        environment['health_score'],
+        normalized_performance,
+        development['setup_health']
+    ]
+    
+    overall_health = round(sum(health_components) / len(health_components), 1)
+    
+    # Generate status
+    if overall_health >= 85:
+        status = 'excellent'
+    elif overall_health >= 70:
+        status = 'good'
+    elif overall_health >= 50:
+        status = 'fair'
+    else:
+        status = 'needs_attention'
+    
+    # Compile recommendations
+    recommendations = []
+    
+    if environment['health_score'] < 80:
+        recommendations.extend([f"Install missing package: {pkg}" for pkg in environment['missing_packages']])
+    
+    if performance['overall_score'] < 50:
+        recommendations.append("Consider hardware upgrade for better ML performance")
+    
+    recommendations.extend(development['recommendations'])
+    
+    # Create comprehensive report
+    report = {
+        'timestamp': datetime.now().isoformat(),
+        'personal_info': personal,
+        'system_info': system,
+        'environment_validation': environment,
+        'performance_benchmarks': performance,
+        'development_setup': development,
+        'overall_health': overall_health,
+        'status': status,
+        'recommendations': recommendations,
+        'report_version': '1.0.0'
+    }
+    
+    return report
+    ### END SOLUTION
+
+# %% [markdown]
+"""
+### 🧪 Unit Test: Enhanced Setup Functions
+
+Test all the new enhanced setup functions:
+"""
+
+# %%
+def test_environment_validation():
+    """Test environment validation function."""
+    print("🔬 Unit Test: Environment Validation...")
+    
+    env_report = validate_environment()
+    
+    # Test return type and structure
+    assert isinstance(env_report, dict), "validate_environment should return a dictionary"
+    
+    # Test required keys
+    required_keys = ['available_packages', 'missing_packages', 'health_score', 'total_checked', 'status']
+    for key in required_keys:
+        assert key in env_report, f"Report should have '{key}' key"
+    
+    # Test data types
+    assert isinstance(env_report['available_packages'], dict), "available_packages should be dict"
+    assert isinstance(env_report['missing_packages'], list), "missing_packages should be list"
+    assert isinstance(env_report['health_score'], (int, float)), "health_score should be number"
+    assert isinstance(env_report['total_checked'], int), "total_checked should be int"
+    assert isinstance(env_report['status'], str), "status should be string"
+    
+    # Test reasonable values
+    assert 0 <= env_report['health_score'] <= 100, "health_score should be between 0 and 100"
+    assert env_report['total_checked'] > 0, "total_checked should be positive"
+    assert env_report['status'] in ['healthy', 'needs_attention'], "status should be valid"
+    
+    print("✅ Environment validation tests passed!")
+    print(f"✅ Environment health: {env_report['health_score']}%")
+
+# %%
+def test_performance_benchmark():
+    """Test performance benchmarking function."""
+    print("🔬 Unit Test: Performance Benchmarking...")
+    
+    benchmark_report = benchmark_performance()
+    
+    # Test return type and structure
+    assert isinstance(benchmark_report, dict), "benchmark_performance should return a dictionary"
+    
+    # Test required keys
+    required_keys = ['cpu_time', 'cpu_score', 'memory_time', 'memory_score', 'overall_score', 'performance_rating']
+    for key in required_keys:
+        assert key in benchmark_report, f"Report should have '{key}' key"
+    
+    # Test data types
+    assert isinstance(benchmark_report['cpu_time'], (int, float)), "cpu_time should be number"
+    assert isinstance(benchmark_report['cpu_score'], (int, float)), "cpu_score should be number"
+    assert isinstance(benchmark_report['memory_time'], (int, float)), "memory_time should be number"
+    assert isinstance(benchmark_report['memory_score'], (int, float)), "memory_score should be number"
+    assert isinstance(benchmark_report['overall_score'], (int, float)), "overall_score should be number"
+    assert isinstance(benchmark_report['performance_rating'], str), "performance_rating should be string"
+    
+    # Test reasonable values
+    assert benchmark_report['cpu_time'] > 0, "cpu_time should be positive"
+    assert benchmark_report['memory_time'] > 0, "memory_time should be positive"
+    assert benchmark_report['cpu_score'] > 0, "cpu_score should be positive"
+    assert benchmark_report['memory_score'] > 0, "memory_score should be positive"
+    assert benchmark_report['overall_score'] > 0, "overall_score should be positive"
+    
+    valid_ratings = ['excellent', 'good', 'fair', 'needs_optimization']
+    assert benchmark_report['performance_rating'] in valid_ratings, "performance_rating should be valid"
+    
+    print("✅ Performance benchmark tests passed!")
+    print(f"✅ Performance rating: {benchmark_report['performance_rating']}")
+
+# %%
+def test_development_setup():
+    """Test development environment setup function."""
+    print("🔬 Unit Test: Development Environment Setup...")
+    
+    setup_report = setup_development_environment()
+    
+    # Test return type and structure
+    assert isinstance(setup_report, dict), "setup_development_environment should return a dictionary"
+    
+    # Test required keys
+    required_keys = ['setup_status', 'recommendations', 'setup_health', 'status']
+    for key in required_keys:
+        assert key in setup_report, f"Report should have '{key}' key"
+    
+    # Test data types
+    assert isinstance(setup_report['setup_status'], dict), "setup_status should be dict"
+    assert isinstance(setup_report['recommendations'], list), "recommendations should be list"
+    assert isinstance(setup_report['setup_health'], (int, float)), "setup_health should be number"
+    assert isinstance(setup_report['status'], str), "status should be string"
+    
+    # Test reasonable values
+    assert 0 <= setup_report['setup_health'] <= 100, "setup_health should be between 0 and 100"
+    assert setup_report['status'] in ['ready', 'needs_configuration'], "status should be valid"
+    
+    print("✅ Development setup tests passed!")
+    print(f"✅ Setup health: {setup_report['setup_health']}%")
+
+# %%
+def test_system_report():
+    """Test comprehensive system report function."""
+    print("🔬 Unit Test: System Report Generation...")
+    
+    report = generate_system_report()
+    
+    # Test return type and structure
+    assert isinstance(report, dict), "generate_system_report should return a dictionary"
+    
+    # Test required keys
+    required_keys = ['timestamp', 'personal_info', 'system_info', 'environment_validation', 
+                    'performance_benchmarks', 'development_setup', 'overall_health', 
+                    'status', 'recommendations', 'report_version']
+    for key in required_keys:
+        assert key in report, f"Report should have '{key}' key"
+    
+    # Test data types
+    assert isinstance(report['timestamp'], str), "timestamp should be string"
+    assert isinstance(report['personal_info'], dict), "personal_info should be dict"
+    assert isinstance(report['system_info'], dict), "system_info should be dict"
+    assert isinstance(report['environment_validation'], dict), "environment_validation should be dict"
+    assert isinstance(report['performance_benchmarks'], dict), "performance_benchmarks should be dict"
+    assert isinstance(report['development_setup'], dict), "development_setup should be dict"
+    assert isinstance(report['overall_health'], (int, float)), "overall_health should be number"
+    assert isinstance(report['status'], str), "status should be string"
+    assert isinstance(report['recommendations'], list), "recommendations should be list"
+    assert isinstance(report['report_version'], str), "report_version should be string"
+    
+    # Test reasonable values
+    assert 0 <= report['overall_health'] <= 100, "overall_health should be between 0 and 100"
+    valid_statuses = ['excellent', 'good', 'fair', 'needs_attention']
+    assert report['status'] in valid_statuses, "status should be valid"
+    
+    print("✅ System report tests passed!")
+    print(f"✅ Overall system health: {report['overall_health']}%")
+
+# %%
+def test_enhanced_integration():
+    """Test integration of all enhanced setup functions."""
+    print("🔬 Integration Test: Enhanced Setup Functions...")
+    
+    # Test that all functions work together
+    env_report = validate_environment()
+    perf_report = benchmark_performance()
+    dev_report = setup_development_environment()
+    system_report = generate_system_report()
+    
+    # Test that system report includes all components
+    assert 'environment_validation' in system_report, "System report should include environment validation"
+    assert 'performance_benchmarks' in system_report, "System report should include performance benchmarks"
+    assert 'development_setup' in system_report, "System report should include development setup"
+    
+    # Test that overall health is calculated
+    assert 'overall_health' in system_report, "System report should include overall health"
+    assert system_report['overall_health'] > 0, "Overall health should be positive"
+    
+    # Test that recommendations are provided
+    assert 'recommendations' in system_report, "System report should include recommendations"
+    assert isinstance(system_report['recommendations'], list), "Recommendations should be a list"
+    
+    print("✅ Enhanced integration test passed!")
+    print("✅ All enhanced setup functions working together!")
+
+# Run all the enhanced tests
+if __name__ == "__main__":
+    test_environment_validation()
+    test_performance_benchmark()
+    test_development_setup()
+    test_system_report()
+    test_enhanced_integration()
+
+    print("\n🎉 Enhanced Setup Module Complete!")
+    print("📈 Progress: Enhanced Setup Module ✓") 
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