GitHub Copilot vs. ChatGPT: Choosing the Right AI Coding Assistant

Hi there! As an artificial intelligence researcher focused on machine learning applications, I‘ve been fascinated by the rapid evolution of AI coding assistants like GitHub Copilot and ChatGPT.

Navi.

In this guide, we‘ll analyze the key strengths and limitations of both tools in-depth. I‘ll also share my perspective on which excels at different use cases based on their technical capabilities.

GitHub Copilot: The Intelligent Coding Pair Programmer

Let‘s start by outlining a few examples that demonstrate GitHub Copilot‘s excellent code generation abilities:

Copilot Writes Complex React Components

When I write a React functional component in VS Code, Copilot autocompletes not just HTML structure, but often suggests relevant state variables and hooks aligning with React best practices:

function ProfileCard() {

  // Copilot suggests:
  const [user, setUser] = useState({});

  return (
    <div>
      {/* Copilot fills in <h1>, <img>, <p> elements */}
    </div>
  );

}

This automatic code writing reduces hours of work crafting components.

Over 60% Suggestion Acceptance Rate

GitHub found over 60% of Copilot‘s code recommendations get used verbatim by developers. This demonstrates the remarkable accuracy and relevance of its code outputs.

But Sometimes Suggests Incorrect Code

However, because Copilot learns from potentially flawed open source code, it occasionally suggests code that doesn‘t compile or introduces bugs. Developers must carefully review its suggestions.

Pros and cons – Copilot boosts productivity tremendously through accurate recommendations, but still requires oversight to catch errors.

ChatGPT: Question Answering and Creative Explorer

Now, let‘s contrast GitHub Copilot against ChatGPT with some examples…

Capable of Basic Coding Tasks

I asked ChatGPT to generate a Python script querying a SQLite database. It produced valid code, but lacked efficiency and best practices:

# ChatGPT‘s script lacks optimizations 
import sqlite3

conn = sqlite3.connect(‘database.db‘)
c = conn.cursor()
c.execute(‘SELECT * FROM table‘)
rows = c.cursor().fetchall()

conn.close()

While usable Python code, ChatGPT didn‘t use libraries like SQLAlchemy or async/await. Copilot would suggest more robust code here.