Handling cross-cutting concerns

Cross-cutting concerns are aspects of a program that affect multiple parts of the system, such as logging, security, data validation, and error handling. They can become scattered throughout a codebase, leading to code duplication and maintenance challenges.

Copilot Chat can help refactor cross-cutting concerns by suggesting the implementation of Aspect-Oriented Programming (AOP) practices or using decorators and middleware patterns to centralize these concerns in a modular, maintainable way.

Example scenario

Imagine you have a Python project that contains multiple service files in which logging occurs. The information that gets logged is defined within each of the individual service files. If the application is modified or extended in future, this design could lead to inconsistency in the content and style of log entries. You can consolidate and centralize the logging behavior to avoid this being distributed across your project.

Here are three files from our example project: the entry point file (main.py), the log message configuration file (logging_config.py), and one of the service files (order_service.py). The example service file shows how log information is defined alongside the business logic for a particular part of the application.

main.py

import logging
from logging_config import setup_logging
from payment_service import PaymentService
from order_service import OrderService
from shipping_service import ShippingService
from inventory_service import InventoryService
from notification_service import NotificationService

def main():
    setup_logging()
    payment_service = PaymentService()
    order_service = OrderService()
    shipping_service = ShippingService()
    inventory_service = InventoryService()
    notification_service = NotificationService()

    # Example usage
    payment_service.process_payment({"amount": 100, "currency": "USD"})
    order_service.place_order({"item": "Book", "quantity": 1})
    shipping_service.ship_order({"item": "Book", "quantity": 1})
    inventory_service.update_inventory("Book", -1)
    notification_service.send_notification("Order has been placed and shipped.")

if __name__ == "__main__":
    main()

logging_config.py

import logging

def setup_logging():
    logging.basicConfig(level=logging.INFO,
                  format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')

order_service.py

import logging

class OrderService:
    def __init__(self):
        self.logger = logging.getLogger(__name__)

    def place_order(self, order):
        self.logger.info("Entering place_order method.")
        try:
            # In here would be the business logic for placing an order
            # ...
            self.logger.info("Order placed successfully.")
        except Exception as e:
            self.logger.error(f"Error in place_order method: {e}")
            raise
        finally:
            self.logger.info("Exiting place_order method.")

Example prompt

Open the project workspace in VS Code or Visual Studio and enter the following prompt in the Copilot Chat view.

@workspace Refactor this project by using AOP to avoid logging that has cross-cutting concerns

Example response

Copilot responds as follows.

To refactor this project using Aspect-Oriented Programming (AOP) to handle logging as a cross-cutting concern, you can use a library like aspectlib. Here is how you can do it:

1. Install aspectlib:

   pip install aspectlib

2. Create an aspect for logging in a new file called logging_aspect.py:

   import aspectlib
   import logging
   
   @aspectlib.Aspect(bind=True)
   def log_method_call(cutpoint, *args, **kwargs):
       logger = logging.getLogger()
       method_name = cutpoint.__name__
       logger.info(f"Entering method: {method_name}")
       try:
           result = yield aspectlib.Proceed
           logger.info(f"Method {method_name} executed successfully.")
           return result
       except Exception as e:
           logger.error(f"Error in method {method_name}: {e}")
           raise
       finally:
           logger.info(f"Exiting method: {method_name}")
   

3. Apply the aspect to the service methods in main.py:

   import logging
   from logging_config import setup_logging
   from payment_service import PaymentService
   from order_service import OrderService
   from shipping_service import ShippingService
   from inventory_service import InventoryService
   from notification_service import NotificationService
   from logging_aspect import log_method_call
   import aspectlib
   
   def main():
       setup_logging()
   
       # Apply logging aspect to service methods
       aspectlib.weave(PaymentService.process_payment, log_method_call)
       aspectlib.weave(OrderService.place_order, log_method_call)
       aspectlib.weave(ShippingService.ship_order, log_method_call)
       aspectlib.weave(InventoryService.update_inventory, log_method_call)
       aspectlib.weave(NotificationService.send_notification, log_method_call)
   
       payment_service = PaymentService()
       order_service = OrderService()
       shipping_service = ShippingService()
       inventory_service = InventoryService()
       notification_service = NotificationService()
   
       # Example usage
       payment_service.process_payment({"amount": 100, "currency": "USD"})
       order_service.place_order({"item": "Book", "quantity": 1})
       shipping_service.ship_order({"item": "Book", "quantity": 1})
       inventory_service.update_inventory("Book", -1)
       notification_service.send_notification("Order has been placed and shipped.")
   
   if __name__ == "__main__":
       main()
   

Copilot then tells you to remove the logging code from all of the service classes, and gives you the code for the revised files. For example, this is the revised version of order_service.py:

class OrderService:
    def place_order(self, order):
        # In here would be the business logic for placing an order
        # ...
        pass

In the refactored version of the project, the same logging operations are performed, but the logging code is centralized in a single file.

Further reading

• "Prompt engineering for GitHub Copilot"
• "Best practices for using GitHub Copilot"