Code reviews often lose their value when they are rushed or unstructured. Without a checklist, it's easy to miss hidden bugs, overlook security gaps, or give vague feedback. A well-structured code review checklist brings discipline, improves coverage, and helps teams catch problems early, improving the code quality and making releases smoother. This blog post gives you a checklist that helps you review code faster, catch issues early, and avoid missing important details.

Functional Correctness and Logic

Requirement verification: Check if the code covers the intended feature or bug fix, including edge cases.

Error handling: Confirm that the code gracefully handles empty inputs, invalid data, or unexpected conditions.

Regression and compatibility: Make sure that all existing tests are passing and that the code is backward compatible.

Code Clarity and Readability

Understandability: Ensure that the code is easy to read and understand, with a clear structure and minimal nesting.

Naming conventions: Check whether the variable, function, and class names are self-explanatory and clearly describe their purpose.

Context-rich comments: Write comments that clarify why a line of code exists, especially when the purpose isn’t obvious at first glance. Some comments just repeat what the code already shows, which is not helpful. But in the example below, the comments explain why each discount is applied, making it easier for reviewers and future code authors to better understand the logic.

def calculate_discount(price, customer_type):
    # VIP customers receive a higher discount as part of our loyalty program
    if customer_type == "VIP":
        return price * 0.8
    # Regular customers get a standard discount to encourage repeat business
    elif customer_type == "regular":
        return price * 0.9
    # New or unknown customers do not receive a discount
    else:
        return price

Simplicity: Make sure that long functions and code blocks are broken down into smaller, clearer sections.

Consistent formatting: Ensure that the code adheres to the team’s style guide and passes linting checks.

Leftover or dead code: Point out debug prints, commented-out blocks, and outdated code that should be cleaned up.

Performance

Performance optimization: Ensure the code avoids unnecessary computation in performance-critical sections.

Redundant I/O: Look for unnecessary database queries, repeated API calls, or inefficient file access.

Caching usage: Ensure caching is used thoughtfully to improve performance without adding complexity.

Cost efficiency: Be aware of changes that could increase cloud spend, like frequent data transfers or compute-heavy operations.

Race conditions: When reviewing the code that runs in parallel (threads or async functions), check whether it handles shared data safely. Look for things like deadlocks, safe queues, or other tools that prevent two parts of the code from changing the same data at the same time.

Security

Input handling: Make sure that external inputs are validated and sanitized to prevent injection or malformed data issues.

Sensitive data protection: Ensure that secrets aren’t exposed in logs or code and that proper secure storage is used.

  • Hardcoding secrets in code: Check whether the author stores sensitive information in environment variables or uses a dedicated secret management service. Example:

# Secure: Loading secrets from environment variables
import os

API_KEY = os.getenv('API_KEY')
DB_PASSWORD = os.getenv('DB_PASSWORD')

  • Logging sensitive data: Ensure that the code masks or redacts sensitive data before logging it. Example:

# Secure: Masking sensitive data in logs
masked_number = credit_card_number[-4:].rjust(len(credit_card_number), '*')
logging.info(f"Processing payment for card number: {masked_number}")

  • Storing sensitive data unencrypted: Check if the code encrypts sensitive data before storing it, and the author manages encryption keys securely and keeps them separate from the encrypted data. Example:

# Secure: Encrypting sensitive data before storing
from cryptography.fernet import Fernet

key = Fernet.generate_key()
cipher = Fernet(key)
encrypted_data = cipher.encrypt(credit_card_number.encode())

store_in_db(encrypted_data)
store_key_securely(key)

Access control: Verify that permission checks are correctly implemented for sensitive operations.

Third-party packages: Check that dependencies are safe, current, and free from known vulnerabilities.

Testing and Observability

Sufficient test coverage: Ensure that new features have tests that verify correctness and expected behavior.

Edge-case handling: Check whether tests include unusual or failure scenarios, not just success paths. For example, when you consider a function that divides two numbers, make sure both success paths and edge cases are included:

  • Common case:

def test_divide_normal():
    assert divide(10, 2) == 5

  • Edge case:

import pytest

def test_divide_by_zero():
    with pytest.raises(ZeroDivisionError):
        divide(10, 0)

All tests passing: Ensure that the change doesn’t break existing tests and doesn’t introduce flakiness.

Observability hooks: Confirm that logs, metrics, or traces are added where needed to enable post-deployment debugging.

Maintainability

Code complexity: If a function or class is hard to follow, overly complex, or does too many things at once, suggest breaking it down. Make sure that each part of the code handles a single, clear task to make it easier to read, test, and maintain. In the following example, each function has a single, clear responsibility:

def process_order(order):
    validate_order(order)
    apply_discount(order)
    shipping = calculate_shipping(order)
    save_order(order)
    send_confirmation(order['customer_email'])

def validate_order(order):
    if not order['items']:
        raise ValueError("No items in order")
    if order['total'] <= 0:
        raise ValueError("Total must be positive")

def apply_discount(order):
    if order.get('discount_code') == "SAVE10":
        order['total'] *= 0.9

def calculate_shipping(order):
    return 0 if order['total'] > 100 else 10

def save_order(order):
    db.save(order)

def send_confirmation(email_address):
    email.send(email_address, "Order confirmation", "Thank you for your order!")

Extensibility: Ensure that the code is structured in a way that allows future modifications without major rewrites.

Up-to-date documentation: Check whether any related documentation, READMEs, or usage examples have been revised alongside the code.

Duplication: Watch for repeated code and move it into a shared function or utility. Ensure that the code follows the DRY (Don’t Repeat Yourself) principle; each piece of code is in one clear, reliable place in the codebase.

Conclusion

A high-quality codebase begins with well-structured code reviews, and the right checklist helps make that happen. Use this guide to catch hidden issues early, improve performance, and make the code review process a lot easier. When teams review with precision and intention, they ship faster, prevent breakdowns before they happen, and build software everyone can rely on.