GitHub Copilot in the Real World: Developer Accelerator or Hype?

By /

When GitHub Copilot launched, it promised to change the way we write code — forever. A year later, does it live up to the hype? In this post, I break down my personal experiences using Copilot across different workflows: from greenfield development to unit testing and writing documentation.

🧠 What Is GitHub Copilot?

GitHub Copilot is an AI-powered coding assistant developed by GitHub and OpenAI. It suggests code completions, snippets, and even entire functions directly in your editor. It works contextually — using your file, your function, and even comments to predict what you’re trying to write next.

🛠️ Real-World Use Cases

  • Boilerplate & Repetitive Code: Great for scaffolding repetitive code like components or test stubs.
  • Writing Unit Tests: Type a comment and Copilot often nails the expected logic.
  • Regex and YAML: Surprisingly effective at writing configs and regex.

💡 Example: Copilot Writing a Test Suite

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

  it('handles negatives', () => {
    expect(add(-1, -2)).toBe(-3);
  });
});

⚠️ Where Copilot Falls Short

  • Struggles with new or internal APIs
  • May suggest insecure or incorrect logic
  • Not consistent with variable naming

🔐 Security Considerations

Never blindly accept Copilot suggestions in authentication, encryption, or input validation. Use linters and security scanning tools to catch issues.

🤖 Copilot vs. ChatGPT for Devs

Feature Copilot ChatGPT
Inline Code Suggestions ✅ ❌
Explaining Code ❌ ✅

🧪 Tips for Better Copilot Use

  • Write comments first to guide suggestions
  • Use meaningful variable/function names
  • Always review before accepting

✅ Verdict

Copilot is a powerful tool when used properly. Treat it like a junior dev: helpful, but in need of review. Paired with good habits and testing, it’s a productivity boost worth adopting.

Scroll to Top