GitHub Copilot: Accelerating Coding?

How useful is GitHub Copilot really, for both beginners and advanced programmers? Following our previous comparison of AI coding assistants, where GitHub Copilot struggled against Claude 3.5 and GPT-4, let’s dive deeper into what Copilot is actually good at – and what it isn’t.

Note: This video reviews an earlier version of GitHub Copilot. While newer versions have been released, the core strengths and limitations discussed here remain relevant. We’ve stopped making version-specific AI tool reviews because these tools update so frequently that comparisons become outdated within months.

The Promise vs. Reality

What GitHub Claims:
– Increased developer productivity
– Better code completion
– Seamless integration with VS Code
– Powered by GPT-4 technology

Reality Check:
– Often uses GPT-3.5 instead of GPT-4
– Study claiming higher productivity was Microsoft-funded with only 95 participants
– Increasing code churn (code needing revision within 2 weeks)
– Struggles with error correction

Where Copilot Shines

Documentation and Understanding:
– Excellent at explaining existing code
– Good at generating documentation
– Helpful for writing README files
– Can provide context for unfamiliar code

This is actually where I’ve found Copilot most useful. When you’re looking at someone else’s code and trying to figure out what it does, Copilot can give you a quick explanation. It’s like having a colleague who can read through code and summarize it for you.

Code Suggestions:
– Works well with clear context
– Can speed up basic coding tasks
– Useful for boilerplate code
– Good at pattern recognition

Major Limitations

Error Handling:
– Often can’t fix its own errors
– Requires manual intervention
– May generate non-working code
– Can introduce subtle bugs

This is the biggest problem. When Copilot generates code with a bug, it usually can’t fix it. You end up going back to Claude or ChatGPT anyway to debug the issue.

Code Quality:
– May hallucinate features
– Needs significant review
– Can generate inefficient solutions
– Sometimes misses edge cases

Best Practices for Using Copilot

For Beginners:
– Use it to learn, not replace learning
– Focus on understanding code explanations
– Verify all generated code
– Consider free alternatives like Claude or ChatGPT

If you’re just learning Python, I wouldn’t recommend paying for Copilot. Use the free tier of Claude or ChatGPT instead – they’re better at explaining concepts and fixing bugs.

For Experienced Developers:
– Provide clear context
– Keep relevant files open
– Use it for documentation
– Don’t rely on it for error fixing

The Productivity Question

Microsoft’s study showed developers were “more productive” with Copilot. But the study was small (95 participants), Microsoft-funded, and didn’t measure code quality – just speed.

The code churn data tells a different story. Code written with AI assistance needs more revisions within two weeks. So yes, you might write code faster, but you’ll also spend more time fixing it.

The Verdict

GitHub Copilot can speed up certain aspects of coding, but it’s not the revolutionary tool some claim it to be. Its strengths lie in code explanation and documentation rather than code generation.

For beginners, free tools like Claude or ChatGPT are more appropriate – they provide better code generation and debugging support. For experienced developers, Copilot can be useful for documentation and boilerplate code, but don’t expect it to replace your problem-solving skills.

Use Copilot for:
– Code understanding and documentation
– Boilerplate generation
– Learning unfamiliar codebases

Don’t use Copilot for:
– Debugging your code
– Complex problem-solving
– Learning programming fundamentals

Related Content

For more on AI coding tools and how to use them effectively:
– AI Coding Assistant Comparison
– Debunking AI Programming Myths

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