AI-Driven Pull Requests: Smarter Reviews, Faster Merges

Introduction

AI is reshaping how we write, review, and merge code — here’s how GitHub Copilot, Snyk, and CodeRabbit are transforming pull requests.

PRs – Real Life example – The Collaborative Library

Let’s take a real-life example — writing a book. An author writes the story. The story goes to an editor — they check for mistakes, suggest changes, and improve it. Once it’s good, the book is added to the library. The final version is cleaner and better because of collaboration.

PRs – Developer Scenario

Now let’s see how this works for developer.

The developer writes code and creates a pull request (PR). A code reviewer looks at the PR and review the code, provide feedback and then approve it.

After the review, the code is merged into the main branch of the project.

PR Review in Action with AI

Now, AI tools can help us review faster and smarter — like having an assistant to help the reviewer.

Along with the code reviewer, we now have AI tools helping out — just like smart assistants.

Think of them as AI teammates — always watching, always helping.

Let’s explore each AI tools and how effective to the project.

Why PR Reviews Matter

Pull Requests, or PRs, are not just about merging code — they’re key for teamwork and code quality.

A good PR review helps catch bugs, share knowledge, and improve everyone’s work.

Now, let’s look at some common challenges with PR reviews:

Delays

• Sometimes PRs sit waiting for review.

• This slows down the entire team and blocks progress.

Inconsistent Reviews

• Some reviewers give detailed feedback, others don’t.

• This leads to uneven code quality.

Human Error

• We’re all human — reviewers might miss a bug, or overlook a bad pattern.

• Mistakes slip through, especially when there’s a time crunch.

Common Challenges

Why Code Review?

Let’s quickly go over why code reviews are important.

Catch bugs early

• Reviews help spot issues before the code goes live.

• It’s easier to fix problems early.

Ensure code quality and consistency

• Everyone writes code a little differently.

• Reviews help make sure the code follows our standards and stays clean.

Share knowledge across the team

• Reviews are a great way to learn from each other.

• It spreads awareness about what’s happening in the codebase.

Keep the code maintainable

• Clean, reviewed code is easier to update in the future.

• It helps future team members understand and work with the code.

What's the current issues here?

Writing useless commit messages hurts collaboration

Examples of Bad Review

Developer feeling tired after bad PRs

This tweet perfectly sums up real-world PR frustrations.

Reviewing 10 lines? — hours of pointing out every small mistake .

Reviewing 500 lines? — one casual ‘Looks good to me.

This is Painful.
Perfect example of how not to do with security checks

How AI Can Fix This

Clearly, code reviews are messy — slow sometimes, rushed other times.

This is exactly where AI can step in and make a real difference

AI tools don’t replace reviewers, but they assist us

How AI is Changing the Game

So, how can we fix these challenges?
AI is stepping in to help make PR reviews smarter, faster, and more consistent.

• GitHub Copilot

• CodeRabbit

• Snyk’s DeepCode AI

• CodiumAI (now Qodo)

• Bito AI Code Review Agent

How AI Enhances PRs

Let’s look at how AI is making Pull Requests smarter:

GitHub Copilot helps suggest better code.

CodeRabbit adds detailed line-by-line comments.

Snyk finds security issues before merging.

AI helps us focus on what matters most — writing good, safe code.

GitHub Copilot

Features

GitHub Copilot helps developers while coding also

• Inline code completion

• AI Chat Assistance

• Copilot Code Review

Under the Hood

Built on OpenAI Codex (GPT-3.5 & GPT-4)

• Predicts next code token using file and code context (Autocompletion)

• Summarizes PRs using code diffs and commit data

Download

For Visual Studio Code

Use Extensions in VS Code

For Xcode

You can use this link for download - GitHub Copilot for Xcode

Github Copilot Pro Access

Check out the link to get access - Copilot plans and benefits

Automated Commit Message Creation

One amazing feature:
Automatic, meaningful commit messages based on code changes.
No more “Update file” commits!

For example, commit messages have a maximum length of 60 chars and should start with a verb in the present tense
You can enhance Copilot's chat responses by providing it with contextual details about your team's workflow, tools, or project specifics.
Instead of manually including this context in every chat query, you can create a custom instructions file that automatically incorporates this information with every chat request.

Custom instructions settings

• github.copilot.chat.codeGeneration.useInstructionFiles: controls whether code instructions from .github/copilot-instructions.md are added to Copilot requests.

• github.copilot.chat.codeGeneration.instructions (Experimental): set of instructions that will be added to Copilot requests that generate code.

• github.copilot.chat.testGeneration.instructions (Experimental): set of instructions that will be added to Copilot requests that generate tests.

• github.copilot.chat.reviewSelection.instructions (Preview): set of instructions that will be added to Copilot requests for reviewing the current editor selection.

• github.copilot.chat.commitMessageGeneration.instructions (Experimental): set of instructions that will be added to Copilot requests that generate commit messages.

Check out: Customize chat responses in VS Code

Pull Request

Go to the project Settings and select the check the checkbox - Request pull request review from Copilot

Rule set

Generating Summary

Check out official documentation - Requesting a review from Copilot

On GitHub.com, create a pull request or navigate to an existing pull request.

Open the Reviewers menu, then select Copilot.

Wait for Copilot to review your pull request. This usually takes less than 30 seconds.Scroll down and read through Copilot's comments.

Copilot always leaves a "Comment" review, not an "Approve" review or a "Request changes" review.

Code Suggestions

Once we integrate the Xcode extension - Copilot, you can give a prompt to generate code.

While writing the code, Github Copilot automatically suggest the line of code. It’s called as autocompletion.

Currently available LLMs in Github Copilot

Code Rabbit

Features

• Generates Summary of the changes

• line-by-line feedback

• AI code Reviewer

Under The Hood

Powered by GPT-4 or Claude 2 (LLMs)

CodeRabbit doesn’t read the entire repository

• Analyzes PR diffs and file context (not full repo)

• Combines semantic diff parsing + LLM reasoning to write comments

Semantic diff parsing, which means: It doesn’t just see text changes, but tries to understand the meaning behind the code changes.

For example, if you refactor a function or change a method name, it can figure out why you did that — not just what changed.

Then it combines this understanding with LLM reasoning — like GPT-4’s smart thinking — to suggest proper review comments, catch mistakes, or recommend improvements.

Data Privacy & Security– Code Rabbit

• No data from code reviews is used to train models

• LLM queries are short-lived (ephemeral)

• Temporary storage with conversation-based embeddings

• Compliant with SOC2 Type II & GDPR

CodeRabbit ensures security and privacy.
No code is used for training. Data is short-lived and GDPR compliant

Refer the below flow diagram for more information

Flow Diagram

Review Flow

GitHub Integration

Once we signed up the Code Rabbit, we can easily integrate with GitHub and it takes only read-only access.

Pull Request

PR Summary

Creating Pull Request Summary is helpful for both developer and code reviewer.

Walkthrough

Code walkthrough will be really helpful for the code reviewer. This will be helpful to approve or reject the PR within the time limit.

Code Review

Potential Issue

Assisting the developers by identifying the potential edge cases, thus helping amount of time.

Refractor Suggestion

Code Refraction is much needed for the developers, thus helps to follow coding standards and best practices

Verification Agent

In case we are using third party frameworks and that might not be latest stable version, thus reminds developers to upgrade it.

Sequence Diagram

Sequence Diagram will be helpful for Junior developers and managers to understand how our project architecture works.

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Snyk

Snyk uses DeepCode AI to find vulnerabilities automatically.
It helps keep your codebase safe while you focus on features.

Features

• AI-driven static analysis tool

• Automated PR checks

• Automated Security Reviewer

Under the Hood

Powered by DeepCode AI

DeepCode AI combines code scanning and machine learning.

It’s trained to spot vulnerabilities, not just obvious mistakes, using smart techniques like ASTs and semantic understanding.

• Static code analysis + machine learning models

• Trained on public vulnerability databases (NVD, Snyk DB)

• Uses semantic analysis and abstract syntax trees (ASTs)

NVD (National Vulnerability Database) and Snyk’s own database

semantic analysis — meaning it understands the logic of the code

Abstract Syntax Trees — basically breaking the code into a structure

Security Code Review Best Practices

Snyk Code Analysis

Snyk’s Code Analysis makes sure your PRs are clean from security risks

Snyk Code Review

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Final Thoughts

Takeaways

• Copilot helps you write code faster

• CodeRabbit ensures the code is clean

• Snyk ensures the code is secure

Together, they build a smarter, safer development pipeline.

What's the problem I see

• Same/duplicate effort

• False Positives & False Negatives

• Limited in Handling Non-Standard Code

• Shallow Understanding of Code Intent

• Bias & Limitations in Training Data

What AI can’t help you?

• Cannot stop you from creating larger PRs

• Cannot understand the entire context of your codebase/Project

• You can’t eliminate human reviews completely

Smarter Reviews, Faster Merges — Thanks to AI

• PRs are teamwork, not just code merges.

• Human + AI = Better Code Reviews.

• Tools like GitHub Copilot, CodeRabbit, and Snyk assist

• Use AI for speed, security, and consistency

• Final responsibility always stays with us — the developers

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