GitHub Copilot Mastery Part 6: Real-World Use Cases That Transform Development

# Debugging a performance issue in data processing
def investigate_performance_issue():
    """
    Ask Copilot: "Create a systematic approach to debug this slow data processing function.
    Include profiling, memory analysis, and bottleneck identification."
    """
    
    # Copilot will suggest a comprehensive debugging approach:
    import cProfile
    import memory_profiler
    import time
    from functools import wraps
    
    def debug_wrapper(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            # Memory before
            mem_before = memory_profiler.memory_usage()[0]
            
            # Time execution
            start_time = time.time()
            
            # Profile execution
            profiler = cProfile.Profile()
            profiler.enable()
            
            try:
                result = func(*args, **kwargs)
                return result
            finally:
                profiler.disable()
                
                # Collect metrics
                execution_time = time.time() - start_time
                mem_after = memory_profiler.memory_usage()[0]
                
                # Report findings
                print(f"Function: {func.__name__}")
                print(f"Execution time: {execution_time:.2f}s")
                print(f"Memory delta: {mem_after - mem_before:.2f}MB")
                
                # Save profile for analysis
                profiler.dump_stats(f"{func.__name__}_profile.prof")
                
        return wrapper
    
    return debug_wrapper

# Analyze and optimize data processing code
class DataAnalyzer:
    def __init__(self, database_connection):
        self.db = database_connection
    
    def analyze_user_behavior(self, start_date, end_date):
        # Current implementation - potentially slow
        users = self.db.execute("""
            SELECT user_id, action_type, timestamp 
            FROM user_actions 
            WHERE timestamp BETWEEN %s AND %s
        """, (start_date, end_date))
        
        results = {}
        for user_id, action, timestamp in users:
            if user_id not in results:
                results[user_id] = {'actions': [], 'patterns': {}}
            
            results[user_id]['actions'].append({
                'type': action,
                'time': timestamp
            })
        
        # Analyze patterns for each user
        for user_id in results:
            results[user_id]['patterns'] = self._analyze_patterns(
                results[user_id]['actions']
            )
        
        return results

/*
Ask Copilot: "This data analysis method is slow for large datasets. 
Suggest performance optimizations including database queries, 
data structures, and processing algorithms."

Copilot will suggest:
- Database query optimizations (indexes, query structure)
- Batch processing strategies
- Memory-efficient data structures
- Parallel processing opportunities
- Caching mechanisms
- Streaming data processing
- Database pagination
- Connection pooling improvements
*/

# Procedural code that needs pattern-based refactoring
def process_order(order_data):
    # Validation
    if not order_data.get('customer_id'):
        raise ValueError("Customer ID required")
    if not order_data.get('items'):
        raise ValueError("Order items required")
    
    # Calculate totals
    subtotal = 0
    for item in order_data['items']:
        subtotal += item['price'] * item['quantity']
    
    # Apply discounts
    discount = 0
    if order_data.get('coupon_code'):
        if order_data['coupon_code'] == 'SAVE10':
            discount = subtotal * 0.1
        elif order_data['coupon_code'] == 'SAVE20':
            discount = subtotal * 0.2
    
    # Calculate tax
    tax = (subtotal - discount) * 0.08
    total = subtotal - discount + tax
    
    # Process payment
    if order_data['payment_method'] == 'credit_card':
        # Credit card processing logic
        pass
    elif order_data['payment_method'] == 'paypal':
        # PayPal processing logic
        pass
    
    # Send confirmation
    send_email(order_data['customer_email'], generate_confirmation(order_data))
    
    return {'order_id': generate_order_id(), 'total': total}

/*
Copilot refactoring request:
"Refactor this procedural order processing code using appropriate design patterns. 
Consider Strategy pattern for discounts and payments, Command pattern for operations, 
and Factory pattern for order creation."

Copilot will create a well-structured solution with:
- Strategy pattern for discount calculations
- Strategy pattern for payment processing  
- Factory pattern for order creation
- Observer pattern for order events
- Proper separation of concerns
- Testable, maintainable code structure
*/

# API endpoint that needs integration testing
@app.route('/api/orders', methods=['POST'])
def create_order():
    try:
        # Validate input
        order_data = request.get_json()
        validate_order_data(order_data)
        
        # Check inventory
        inventory_check = inventory_service.check_availability(order_data['items'])
        if not inventory_check.available:
            return jsonify({'error': 'Insufficient inventory'}), 400
        
        # Process payment
        payment_result = payment_service.process_payment(
            order_data['payment_info'],
            calculate_total(order_data)
        )
        
        if not payment_result.success:
            return jsonify({'error': 'Payment failed'}), 400
        
        # Create order
        order = order_service.create_order(order_data, payment_result.transaction_id)
        
        # Update inventory
        inventory_service.reserve_items(order_data['items'])
        
        # Send confirmations
        notification_service.send_order_confirmation(order)
        
        return jsonify(order.to_dict()), 201
        
    except ValidationError as e:
        return jsonify({'error': str(e)}), 400
    except Exception as e:
        logger.error(f"Order creation failed: {e}")
        return jsonify({'error': 'Internal server error'}), 500

/*
Copilot integration test request:
"Create comprehensive integration tests for this order creation endpoint. 
Include database transactions, external service mocking, and end-to-end scenarios."

Copilot will create:
- Test database setup and cleanup
- Mock external services (payment, inventory, notifications)
- Happy path end-to-end test
- Error handling tests for each failure point
- Database transaction rollback tests
- Concurrent request handling tests
- Rate limiting tests
- Authentication and authorization tests
- Input validation boundary tests
- Performance tests under load
*/

# Data handling code that needs privacy analysis
class CustomerService:
    def export_customer_data(self, customer_id):
        # GDPR data export functionality
        customer = self.get_customer(customer_id)
        
        data = {
            'personal_info': {
                'name': customer.name,
                'email': customer.email,
                'phone': customer.phone,
                'address': customer.address,
                'date_of_birth': customer.date_of_birth,
                'ssn': customer.ssn  # Potential issue
            },
            'orders': self.get_customer_orders(customer_id),
            'payments': self.get_payment_history(customer_id),
            'support_tickets': self.get_support_history(customer_id),
            'device_info': self.get_device_tracking(customer_id),
            'behavioral_data': self.get_analytics_data(customer_id)
        }
        
        # Send via email - potential security issue
        self.email_service.send_data_export(customer.email, data)
        
        return data
    
    def anonymize_customer(self, customer_id):
        # GDPR right to be forgotten
        customer = self.get_customer(customer_id)
        
        # Simple anonymization - potentially inadequate
        customer.name = "ANONYMIZED"
        customer.email = f"anon_{customer_id}@deleted.com"
        customer.phone = None
        customer.address = None
        
        self.customer_repo.save(customer)

/*
Copilot privacy analysis request:
"Analyze this customer data handling code for GDPR compliance, data protection issues, 
and privacy risks. Suggest improvements for secure data export and proper anonymization."

Copilot will identify privacy concerns:
- Excessive data collection (SSN in export)
- Insecure data transmission (email export)
- Inadequate anonymization techniques
- Missing consent verification
- No data retention policy enforcement
- Potential data correlation risks
- Missing audit trails
- Insufficient access controls

It will suggest comprehensive improvements:
- Data minimization principles
- Secure file transfer mechanisms
- Proper anonymization vs. pseudonymization
- Consent management integration
- Data retention policy automation
- Privacy impact assessment guidelines
- Secure data deletion procedures
- Privacy by design recommendations
*/