Copilot Pull Requests — Current State

The landscape of software development is rapidly evolving, with AI becoming an increasingly integrated part of our daily workflows. Among the most impactful tools is GitHub Copilot, which has fundamentally changed how many of us write code. But as engineers, we know coding is only one part of the equation. What about the crucial process of creating pull requests (PRs)? Can Copilot, in its current state, truly automate or significantly enhance your PR descriptions?

Let's dive into the practical realities, exploring where Copilot shines, where it falls short, and what this means for your workflow.

Copilot's Core Strengths and Limitations for PRs

Copilot excels at code generation and, to a lesser extent, code explanation within a limited context. It's brilliant for:

• Autocompleting code: Filling in boilerplate, suggesting functions, or completing lines based on context.
• Generating new code: Given a clear prompt or existing code, it can draft new functions, tests, or even small components.
• Explaining snippets: You can ask it to explain what a specific function or block of code does.

These capabilities are undeniably powerful. But when it comes to pull requests, the challenge isn't just about understanding a single function; it's about synthesizing a coherent narrative from a potentially large and complex git diff, understanding the intent behind the changes, and communicating that effectively to your team.

Copilot's primary limitation here is its scope. While its context window has grown, it fundamentally operates on the code you're currently viewing or have explicitly provided. It doesn't inherently understand:

• The entire git diff across multiple files without you manually feeding it.
• The broader project context, such as the associated Jira ticket, design document, or feature specification.
• Your team's specific PR template, required sections (e.g., "Test Plan," "Risk Assessment"), or conventions.
• The "why" behind the changes beyond what can be inferred from the code itself.

How Engineers Are (Indirectly) Using Copilot for PRs

Despite these limitations, engineers are resourceful. Many are already leveraging Copilot to assist with parts of their PR description process, albeit often in a piecemeal fashion.

Summarizing Individual Code Changes

One common approach is to use Copilot Chat or similar features to get explanations for specific parts of your diff. When you've made a change to a particular file or function, you might feed that specific diff chunk to Copilot.

Concrete Example 1: Summarizing a single file's changes

Imagine you've refactored a React component to use a new custom hook. After running git diff src/components/UserAvatar.tsx, you get a diff like this:

--- a/src/components/UserAvatar.tsx
+++ b/src/components/UserAvatar.tsx
@@ -1,10 +1,11 @@
 import React from 'react';
 import { User } from '../types';
-import { useAuth } from '../hooks/useAuth';
+import { useCurrentUser } from '../hooks/useCurrentUser'; // New hook

 interface UserAvatarProps {
   userId: string;
 }

 const UserAvatar: React.FC<UserAvatarProps> = ({ userId }) => {
-  const { user } = useAuth();
+  const { user } = useCurrentUser(); // Using new hook
   // ... rest of component logic
   return (
     <img src={user?.avatarUrl || '/default-avatar.png'} alt={user?.name || 'User Avatar'} />
   );
 };

 export default UserAvatar;

You could then copy this specific diff (or a larger chunk if your editor's Copilot integration allows) into Copilot Chat and prompt it: "Summarize the changes in this diff for a pull request description."

Copilot's likely response would be something concise and accurate for that specific change:

"This change updates the UserAvatar component to replace the useAuth hook with useCurrentUser for fetching user data, improving separation of concerns."

Pitfall: While helpful for individual files, this approach quickly becomes tedious for larger PRs that touch many files. You'd have to manually select, copy, paste, and prompt for each significant change, then synthesize all those individual summaries into a cohesive overall description. This manual aggregation negates much of the potential time savings.

Drafting Specific PR Sections

Another way engineers use Copilot is for brainstorming or drafting specific sections of a PR description, like a test plan or a high-level summary. Here, you're providing the context to Copilot, rather than expecting it to infer it directly from the diff.

**Concrete Example 2: Generating