Ensuring that the code follows the existing patterns and best practices of the project is crucial for maintaining software quality. Below, I will use an example to demonstrate how to identify code that does not align with project best practices and how to improve it.

Old Code Example

Suppose we have a backend service for an online store, which includes a class for managing product inventory. Here is the old code:

class Inventory:
    def __init__(self):
        self.stock = {}

    def add_product(self, product_id, quantity):
        if product_id in self.stock:
            self.stock[product_id] += quantity
        else:
            self.stock[product_id] = quantity

    def remove_product(self, product_id, quantity):
        if product_id in self.stock:
            if self.stock[product_id] >= quantity:
                self.stock[product_id] -= quantity
            else:
                print("Not enough stock!")
        else:
            print("Product not found!")

Code Issues

• Use of Print Statements for Error Handling: Using print statements to handle errors is not a good practice, especially in production environments, as it hinders error tracking and log management.

• Inconsistent Error Handling: The error handling in add_product and remove_product is inconsistent, which can lead to difficulties in maintaining the code.

Project Best Practices

Assume that the project’s best practices include the following:

• Use exception handling to manage errors.

• Use the logging library to record errors and important information, rather than using print.

• Use more expressive method names in classes.

Improved Code

import logging

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

class Inventory:
    def __init__(self):
        self.stock = {}

    def increment_stock(self, product_id, quantity):
        if product_id in self.stock:
            self.stock[product_id] += quantity
        else:
            self.stock[product_id] = quantity
        logging.info(f"Stock added: {quantity} units of {product_id}. Total stock: {self.stock[product_id]}.")

    def decrement_stock(self, product_id, quantity):
        try:
            if self.stock[product_id] >= quantity:
                self.stock[product_id] -= quantity
                logging.info(f"Stock removed: {quantity} units of {product_id}. Remaining stock: {self.stock[product_id]}.")
            else:
                raise ValueError("Insufficient stock available.")
        except KeyError:
            logging.error(f"Attempt to remove non-existent product {product_id} from stock.")
            raise KeyError("Product not found in inventory.")
        except ValueError as e:
            logging.error(str(e))
            raise

Explanation of Code Improvements

1. Logging:
   The logging library is used instead of print, allowing errors and important operations to be properly logged for monitoring and troubleshooting.

2. Exception Handling:
   Errors are now handled by raising exceptions, which provides better error management compared to just printing error messages.

3. Method Renaming:
   The methods add_product and remove_product are renamed to increment_stock and decrement_stock to make the method names more expressive and better reflect their behavior.

This example shows how to improve code according to project best practices, enhancing the maintainability, consistency, and scalability of the code.