As computer experts, you’re likely accustomed to using a wide range of tools to optimize your workflow, boost productivity, and streamline development. In recent years, Artificial Intelligence (AI) has revolutionized how we approach coding, and GitHub Copilot is one of the leading tools to embrace this change.

GitHub Copilot, powered by OpenAI’s Codex model, has emerged as a powerful AI assistant designed to enhance the coding experience. Whether you’re an experienced developer, a system architect, or an AI engineer, Copilot can be an invaluable tool to accelerate your programming tasks. However, to maximize its potential, you need to understand how to use it effectively.

In this blog post, we’ll explore how to use GitHub Copilot like a pro. From configuring the tool to leveraging its advanced features and best practices, we’ll provide actionable insights to make Copilot an integral part of your development process.

1. Setting Up GitHub Copilot

Getting Started with GitHub Copilot

Before you can start using Copilot effectively, you need to set it up. GitHub Copilot works as a plugin within popular code editors, such as Visual Studio Code (VS Code). If you haven’t already, follow these steps to install it:

Install Visual Studio Code (VS Code): If you haven’t installed VS Code yet, you can download it here.
Install GitHub Copilot Extension: Once VS Code is installed, head to the Extensions view (Ctrl+Shift+X), search for “GitHub Copilot,” and click Install.

Note: GitHub Copilot requires a subscription after a free trial period. Make sure to connect it to your GitHub account for full access.

Once installed, you’ll see Copilot actively suggesting code snippets, completing functions, and offering autocomplete solutions tailored to the context of your project.

Understanding GitHub Copilot’s Setup and Configuration

GitHub Copilot doesn’t require much initial configuration, but there are several settings you can adjust to make it work best for you:

Enable/Disable Inline Suggestions: You can turn on or off inline suggestions directly in VS Code. This allows you to control when you want Copilot to pop up with code suggestions.
Fine-tune Suggestions: In settings, you can choose whether Copilot suggests full functions, classes, or smaller code snippets.
Choose Language Specific Settings: Copilot supports various languages such as Python, JavaScript, Go, and TypeScript. You can modify settings based on the language you’re coding in to get more contextually relevant suggestions.

By configuring these settings, you can tailor GitHub Copilot’s behavior to match your development style and project needs.

2. Leveraging Copilot’s Autocomplete Feature for Faster Coding

GitHub Copilot’s autocomplete feature is one of the most powerful aspects of this tool. Rather than typing entire blocks of code manually, Copilot suggests contextually relevant code completions in real-time. This feature is especially useful when writing repetitive code or boilerplate, reducing the time spent on mundane tasks.

For example, when writing a Python function to interact with an API, Copilot might suggest the necessary libraries, the function signature, and even the structure for handling HTTP requests. You can simply accept the suggestions or modify them according to your project’s needs.

Real-World Example: Python API Call

Let’s say you’re writing Python code to call an API. With Copilot, you can start typing something like:

import requests
response = requests.get("https://api.example.com/data")

Copilot will immediately suggest the next steps, such as handling the response, checking for errors, or parsing the data into a usable format.

Best Practices for Using Autocomplete

Don’t Accept Everything: While Copilot is incredibly powerful, it’s still important to review suggestions. Sometimes, it may suggest something that’s syntactically correct but doesn’t align with your requirements.
Learn from Suggestions: Copilot’s suggestions are often based on best practices and commonly used code patterns. This can be a valuable learning opportunity, especially when exploring new libraries or frameworks.
Use for Repetitive Code: Copilot excels in situations where you need to write repetitive code or patterns that don’t change much across projects, such as utility functions, logging, or setup code.

3. Understanding Contextual Code Suggestions

GitHub Copilot doesn’t just suggest random code snippets. It uses the context of the code you’ve written to offer suggestions that make sense. This means it takes into account your variable names, function signatures, and even comments in your code. The more context you provide, the better Copilot’s suggestions will be.

For example, let’s say you’re working on a Node.js project and have defined a function to handle user authentication:

function authenticateUser(user, password) {
    // Copilot will suggest relevant code here based on its understanding
}

Copilot will recognize that the function handles user authentication and suggest relevant snippets for hashing passwords, validating credentials, or integrating with authentication services.

Real-World Example: Refactoring Code

Suppose you’re refactoring a function that processes an array of objects. Copilot can suggest optimizations or help refactor the code by offering alternatives that you might not have considered, such as using array functions like map, filter, or reduce.

4. GitHub Copilot for Testing and Debugging Code

GitHub Copilot isn’t just for writing code; it can also help with testing and debugging. When you write unit tests, Copilot can help suggest assertions, mock data, and edge cases you might have missed.

Real-World Example: Writing Tests for a Simple Function

function add(a, b) {
    return a + b;
}

When you begin writing a test, Copilot can automatically suggest test cases based on the function’s behavior:

test('adds two numbers', () => {
    expect(add(2, 3)).toBe(5);
});

This reduces the time spent writing tests and ensures that your functions are thoroughly tested for common edge cases.

Best Practices for Using Copilot in Testing

Use for Test Automation: Copilot can help generate tests for the entire codebase, especially for edge cases that you might overlook.
Double-Check Test Logic: Copilot’s suggested tests are often a great starting point, but always review the logic to ensure it aligns with your specific use case.
Ensure Full Coverage: While Copilot can generate tests for basic scenarios, make sure your code has full test coverage. Use tools like Jest, Mocha, or PyTest in combination with Copilot to ensure your code is robust.

5. Optimizing Copilot for Large Codebases

In large-scale software projects, it’s essential to manage Copilot’s suggestions efficiently. Copilot can sometimes become overwhelming when it suggests solutions that don’t align with your architectural decisions.

Here’s how to make the most of Copilot in larger codebases:

Use Copilot in Small Chunks: Break down your work into smaller tasks and use Copilot for specific, manageable pieces of code.
Apply the Right Context: Provide clear and specific comments, such as TODOs or function descriptions, to help Copilot generate more accurate suggestions.
Pair with Static Analysis Tools: Tools like ESLint for JavaScript or Pylint for Python can be used alongside Copilot to catch potential issues that Copilot may overlook.

Real-World Example: Refactoring Large Functions

In a large project, refactoring a 300-line function can be daunting. Copilot can help break this down into smaller parts by suggesting modular code blocks for each function, making it easier to maintain and improve.

6. Copilot in Collaborative Development

One of the key advantages of GitHub Copilot is its ability to enhance collaboration. While working with teams, Copilot’s suggestions can help align team members on best practices, coding standards, and more. By using Copilot’s suggestions as a team, you can ensure consistency across your codebase.

Code Review Assistance: Copilot can help reviewers spot areas where improvements can be made or suggest better solutions during the code review process.
Team-wide Consistency: With Copilot offering standardized code suggestions, teams can maintain consistent code quality and architecture.

7. Advanced Features: Exploring the Full Potential of Copilot

GitHub Copilot is packed with advanced features that can further enhance your coding experience. These features include:

Multi-line Suggestions: Copilot can now suggest longer blocks of code, such as entire functions or methods, based on the context.
Copilot Labs: GitHub also offers “Copilot Labs” for experimenting with experimental features. These include tools like code translation between languages and refactoring suggestions.
Copilot for Documentation: Copilot can help generate docstrings for functions or entire modules, ensuring that your code is well-documented and easier to maintain.

Real-World Example: Using Copilot for Documentation

def calculate_total(price, tax_rate):
    """
    Calculate the total price including tax.

    Parameters:
    - price: The original price.
    - tax_rate: The applicable tax rate.

    Returns:
    - The total price after tax.
    """
    return price + (price * tax_rate)

By using Copilot in this way, you can ensure that your code is well-documented with minimal effort.

How to Use Copilot Effectively: A Guide for Computer Experts