Claude Code - Code Analysis

Overview

Estimated time: 25–35 minutes

Learn to leverage Claude's advanced reasoning capabilities for comprehensive code analysis, refactoring, documentation generation, and architectural reviews.

Learning Objectives

• Perform thorough code reviews using Claude's analysis capabilities
• Generate comprehensive documentation and architectural insights
• Identify code smells, security issues, and performance bottlenecks
• Execute complex refactoring operations with AI assistance

Prerequisites

• Basic Claude usage (see introduction)
• Understanding of software architecture principles
• Experience with code review processes

Code Review & Analysis

Comprehensive Code Analysis

Please analyze this code for:
1. Code quality and maintainability
2. Performance implications
3. Security vulnerabilities
4. Design patterns and architectural concerns
5. Testing gaps
6. Documentation needs

[Paste your code here]

Focus on providing specific, actionable recommendations with examples.

Architecture Review

Review this system architecture:

```python
# E-commerce microservices structure
services/
├── user-service/
├── product-service/
├── order-service/
├── payment-service/
└── notification-service/
```

Analyze:
- Service boundaries and responsibilities
- Inter-service communication patterns
- Scalability considerations
- Potential single points of failure
- Recommended improvements

Advanced Refactoring

Legacy Code Modernization

Transform this legacy code to modern patterns:
1. Extract interfaces/abstractions
2. Implement dependency injection
3. Add proper error handling
4. Improve naming conventions
5. Add comprehensive tests
6. Update to latest language features

[Paste legacy code]

Refactor this code to use appropriate design patterns:
- Factory pattern for object creation
- Strategy pattern for algorithms
- Observer pattern for events
- Repository pattern for data access

Explain pattern choice reasoning.

Performance Optimization

Optimize this code for performance:

```javascript
// Current implementation with performance issues
function processLargeDataset(data) {
  let results = [];
  for (let i = 0; i < data.length; i++) {
    for (let j = 0; j < data[i].items.length; j++) {
      if (data[i].items[j].status === 'active') {
        results.push(expensiveOperation(data[i].items[j]));
      }
    }
  }
  return results;
}
```

Provide optimized version with:
1. Time complexity analysis
2. Memory usage improvements
3. Algorithm optimization
4. Caching strategies
5. Parallel processing options

Security Analysis

Vulnerability Assessment

Security review checklist for this code:

```python
def user_login(request):
    username = request.POST.get('username')
    password = request.POST.get('password')
    
    user = User.objects.filter(username=username).first()
    if user and user.password == password:
        session['user_id'] = user.id
        return redirect('/dashboard')
    return render('login.html', {'error': 'Invalid credentials'})
```

Check for:
- SQL injection vulnerabilities
- Authentication bypass risks
- Password handling security
- Session management issues
- Input validation gaps
- CSRF protection needs

Secure Coding Recommendations

Documentation Generation

API Documentation

Generate comprehensive API documentation for this endpoint:

```python
@app.route('/api/users//orders', methods=['GET'])
def get_user_orders(user_id):
    # Implementation here
    pass
```

Include:
- Endpoint description and purpose
- Request/response schemas
- Authentication requirements
- Error codes and messages
- Usage examples
- Rate limiting information

Code Documentation

Add comprehensive docstrings and comments to this complex algorithm:

```python
def dijkstra(graph, start):
    distances = {node: float('infinity') for node in graph}
    distances[start] = 0
    unvisited = set(graph.keys())
    
    while unvisited:
        current = min(unvisited, key=lambda node: distances[node])
        unvisited.remove(current)
        
        for neighbor, weight in graph[current].items():
            distance = distances[current] + weight
            if distance < distances[neighbor]:
                distances[neighbor] = distance
    
    return distances
```

Include:
- Function purpose and algorithm explanation
- Parameter descriptions with types
- Return value documentation
- Time/space complexity analysis
- Usage examples

Testing Strategy

Test Case Generation

Generate comprehensive unit tests for:
- Happy path scenarios
- Edge cases and boundary conditions
- Error conditions and exceptions
- Mock dependencies
- Test data setup/teardown

Create integration tests covering:
- API endpoints with real data
- Database interactions
- External service integrations
- End-to-end user workflows
- Performance benchmarks

Test-Driven Development

Help me implement TDD for this feature:

Feature: User password reset functionality

Requirements:
1. User enters email address
2. System validates email exists
3. Sends secure reset token via email
4. Token expires after 1 hour
5. User can reset password with valid token

Generate:
1. Test cases for each requirement
2. Implementation structure
3. Mock strategies for email service
4. Security considerations

Code Quality Metrics

Complexity Analysis

Analyze this code for quality metrics:

```javascript
function complexBusinessLogic(user, order, inventory) {
  if (user.type === 'premium') {
    if (order.total > 100) {
      if (inventory.hasItem(order.itemId)) {
        if (inventory.getQuantity(order.itemId) >= order.quantity) {
          if (user.creditScore > 700) {
            return processOrder(order, 0.15); // 15% discount
          } else {
            return processOrder(order, 0.10); // 10% discount
          }
        }
      }
    }
  } else if (user.type === 'standard') {
    // Similar nested logic...
  }
  return null;
}
```

Evaluate:
- Cyclomatic complexity
- Nesting depth
- Code duplication
- Maintainability index
- Suggested refactoring approach

Advanced Analysis Techniques

Data Flow Analysis

Trace data flow through this system and identify potential issues:

```python
class OrderProcessor:
    def __init__(self):
        self.orders = []
        self.processed_orders = []
    
    def add_order(self, order_data):
        # Process and validate order
        processed = self.validate_order(order_data)
        if processed:
            self.orders.append(processed)
            self.notify_warehouse(processed)
    
    def validate_order(self, order):
        # Validation logic
        pass
    
    def notify_warehouse(self, order):
        # Warehouse notification
        pass
```

Analyze:
- Data transformation points
- Validation boundaries
- Error propagation paths
- Side effect locations
- Concurrency considerations

Dependency Analysis

Best Practices

Checks for Understanding

Exercises