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Little Grasshopper: “Sister Piao, I’ve recently taken over a large old project with thousands of lines of code, and it’s overwhelming. Is there a good way to help me quickly understand and refactor this code?”
Sister Piao: “Haha, don’t worry, Little Grasshopper. I just happen to have a magic tool for you, which is Cursor! It’s a powerful AI-assisted programming tool specifically designed to help developers tackle complex coding issues. Let me teach you how to conquer this big project using Cursor!”
Alright, let’s take a look at how to use Cursor to refactor a large codebase. Cursor, as a powerful AI-assisted programming tool, can significantly improve our code understanding and refactoring efficiency.
1. Code Understanding
First, we need to quickly understand the overall structure and key functionalities of the project. Cursor’s natural language query feature can help us get information quickly.
“`python
# In Cursor, we can ask:
# “Explain the main functions and structure of this project”
# Cursor will analyze the entire project and provide a response like:
# “This is an online shopping project, mainly consisting of the following modules:
# 1. User Management (user_management.py)
# 2. Product Management (product_management.py)
# 3. Order Processing (order_processing.py)
# 4. Payment System (payment_system.py)
# 5. Database Operations (database_operations.py)
# The main functions include user registration and login, product browsing and purchasing, order generation and processing, online payment, etc.”
“`
With this approach, we can quickly understand the overall structure of the project, laying the groundwork for subsequent refactoring work.
2. Code Optimization
Next, we can use Cursor’s code analysis feature to identify areas that need optimization.
“`python
# In Cursor, we can ask:
# “Analyze the order_processing.py file to find potential performance bottlenecks”
# Cursor might respond with:
# “In the order_processing.py file, there are several performance issues:
# 1. There are repeated database queries in the loop from lines 78-95, which can be optimized to batch queries
# 2. The order status updates from lines 120-150 use multiple individual update operations, which can be combined into a single batch update
# 3. Frequent log writing in lines 200-220 can consider using buffered or asynchronous writing”
# Then, we can ask Cursor to help us optimize these issues:
# “Please help me optimize the loop from lines 78-95 to use batch queries instead of repeated queries”
# Cursor will generate the optimized code:
def process_orders(order_ids):
# Original code
# for order_id in order_ids:
# order = db.query(Order).filter(Order.id == order_id).first()
# product = db.query(Product).filter(Product.id == order.product_id).first()
# # Process order…
# Optimized code
orders = db.query(Order).filter(Order.id.in_(order_ids)).all()
product_ids = [order.product_id for order in orders]
products = db.query(Product).filter(Product.id.in_(product_ids)).all()
product_map = {product.id: product for product in products}
for order in orders:
product = product_map[order.product_id]
# Process order…
“`
In this way, we can gradually optimize the performance bottlenecks in the project and improve code quality.
3. Code Refactoring
After understanding and optimizing the code, we can start larger-scale code refactoring. Cursor’s code generation feature can help us rewrite parts of the code.
“`python
# Suppose we want to refactor the user authentication system, we can ask Cursor:
# “Refactor the user authentication system to use JWT instead of the existing session authentication”
# Cursor will generate the new authentication system code:
import jwt
from datetime import datetime, timedelta
SECRET_KEY = “your-secret-key”
ALGORITHM = “HS256”
def create_access_token(data: dict, expires_delta: timedelta = None):
to_encode = data.copy()
if expires_delta:
expire = datetime.utcnow() + expires_delta
else:
expire = datetime.utcnow() + timedelta(minutes=15)
to_encode.update({“exp”: expire})
encoded_jwt = jwt.encode(to_encode, SECRET_KEY, algorithm=ALGORITHM)
return encoded_jwt
def verify_token(token: str):
try:
payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])
return payload
except jwt.PyJWTError:
return None
# Use in routes
@app.post(“/login”)
def login(username: str, password: str):
user = authenticate_user(username, password)
if not user:
raise HTTPException(status_code=400, detail=”Incorrect username or password”)
access_token_expires = timedelta(minutes=ACCESS_TOKEN_EXPIRE_MINUTES)
access_token = create_access_token(
data={“sub”: user.username}, expires_delta=access_token_expires
)
return {“access_token”: access_token, “token_type”: “bearer”}
“`
With this approach, we can quickly refactor large blocks of code while ensuring the quality and security of the new code.
4. Code Testing
After refactoring, we need to ensure the correctness of the new code. Cursor can help us generate unit tests.
“`python
# We can ask Cursor to generate tests like this:
# “Generate unit tests for the new JWT authentication system”
# Cursor will generate test code like:
import pytest
from fastapi.testclient import TestClient
from main import app
client = TestClient(app)
def test_login_success():
response = client.post(“/login”, json={“username”: “testuser”, “password”: “testpass”})
assert response.status_code == 200
assert “access_token” in response.json()
assert response.json()[“token_type”] == “bearer”
def test_login_fail():
response = client.post(“/login”, json={“username”: “wronguser”, “password”: “wrongpass”})
assert response.status_code == 400
assert response.json()[“detail”] == “Incorrect username or password”
def test_protected_route():
# First, get a valid token
login_response = client.post(“/login”, json={“username”: “testuser”, “password”: “testpass”})
token = login_response.json()[“access_token”]
# Then, use the token to access a protected route
response = client.get(“/protected”, headers={“Authorization”: f”Bearer {token}”})
assert response.status_code == 200
# Try to access without token
response = client.get(“/protected”)
assert response.status_code == 401
“`
In this way, we can quickly generate comprehensive unit tests to ensure that the refactored code works correctly.
Little Grasshopper: “Wow, Sister Piao, this is amazing! Refactoring code with Cursor is like adding wings to a tiger!”
Sister Piao: “That’s right, Little Grasshopper. Cursor is indeed a great assistant for developers. But remember, AI is just a tool; ultimately, you need to understand the code and make decisions. Practice more, and you’ll soon be able to use Cursor to improve your development efficiency!”
By using Cursor’s various features, we can greatly enhance the efficiency and quality of code refactoring. From code understanding, optimization, refactoring to testing, Cursor can provide strong support. I hope this tutorial helps you better use Cursor to handle large project refactoring tasks. Remember, no matter how good the tool is, we need to continually learn and practice to truly unleash its power. Wish you smooth refactoring and soaring code!