Using GitHub Copilot for Smarter Code Reviews: Tips & Best Practices
Why AI-Assisted Code Reviews Matter Today
The quick advancements in Artificial Intelligence have taken the market by storm in the last few years, most of all in the programming industry, which has been the most impacted by it. Not only are developers more open to technological innovations, but AI has also proven to be reliable in coding sessions, understanding complex code logic and design patterns, etc. So, it’s all become a staple in the industry, and there’s no going back anymore: these tools are simply too good and too valuable to ignore. In fact, developers who refuse to do so will fall behind, and one of the most important processes in which AI can be useful is code review automation.
Using Artificial Intelligence tools for automated code review can bring several advantages to your development process, making it faster and more reliable. It can foresee many problems that might arise from a flawed code, such as performance issues, hidden bugs, code smells, technical debt, and more. It is, then, a very valuable and usually needed third opinion on a pull request, other than that of the developer and the reviewer, making the review faster, more profound, and more detailed as well. All of this will improve the code quality of your projects, keeping your codebase reliable and optimized.
And in this context, GitHub Copilot is one of the best AI tools for software developers, as it can easily integrate with VSCode and be configured in your GitHub CI/CD pipeline. It can automatically review your pull requests once they are created and suggest modifications, reducing the risk of human mistakes, bringing your services to their best state with modern development technologies.
How to Use GitHub Copilot for Code Reviews
One of the most important features GitHub Copilot can offer you and your company is its assisted code review, functioning as an assistant to aid a reviewer in catching hidden bugs and code smells. It can, for instance, understand deep complexities that might lead to problems in certain edge cases; understand logic to catch what’s missing; and, at the end of the day, it is a very well-received assistant for reviewing.
To do so, you must first of all log in with your GitHub account on the native VSCode extension for Copilot, so you can gain access to the free tier or choose whether or not to subscribe already. But, in case you don’t have that much experience with AI yet, then you really should test the workflow with it before actually choosing to pay: not everyone will immediately adapt to this tool, so it’s very worth testing before. The free tier and free trial are very generous towards the developers, allowing them to use all the paid functions for a week or two, and that’s more than enough time to learn how to review your PRs with Copilot.
Now, we can move forward with how to use our AI code review tool. We will discuss it deeply in the next section, but here’s a quick introduction: first of all, you must provide your agent with the code context, explaining to it which part it should look at and understand. You must then explain what it does or at least should do (that is, the purpose of the code and how you expect it to work), set up a prompt, and ask it to review your code. Now, let’s understand a little better how exactly we can make it as optimized as possible, so you can bring your AI programming assistant to its maximum power.
A Practical GitHub Copilot Code Review Workflow
Now, here’s a quick guide on how to use GitHub Copilot for code reviews to guarantee a performant and bug-free application.
Step 1: Analyze the Pull Request Context
To start your Copilot code review, first of all, you must give it a very clear and concise context as to what it should understand, process, and analyze. To do this, you must link your code changes to the chatbot so it can understand which lines to parse; usually, in VSCode, you can do this with the shortcut <Ctrl + i> or <Cmd + i>, or by using an @ at the chat to link files. You can, then, request a summary of the changes, which will let you quickly understand exactly what is going on, what the purpose of the code is, and what it does.
Here’s an example prompt for this part:
You are performing an AI-assisted code review.
Below is a pull request diff.
Task 1:
- Provide a clear and concise summary of the changes.
2. Explain the intent of the modifications.
3. Identify which parts of the system are affected (modules, layers, dependencies).
4. Highlight any architectural or design changes.
5. Mention potential impact on performance, security, or maintainability.
Do not suggest fixes yet. Only summarize and analyze the scope.
Pull Request Diff:
——————-
[PASTE DIFF HERE]
——————-
Step 2: Request a Risk Assessment
Now, here’s the second prompt, where we’ll use AI to find bugs. In this important step, we will learn about possible risks and stop hidden bugs or performance problems from going into production. This is probably the most important step because it’s where the real code review happens. You need to understand exactly what was added, how the new code works, and what it should or shouldn’t do. One of the best things about AI-powered software development is that it can keep your codebase’s quality high and find hidden bugs before they cause problems.
This is an example of a well-organized prompt that can help you with this. If you don’t know exactly what you want your assistant to do, you can ask ChatGPT to help you come up with a prompt.
Now look at the same pull request in great detail from a technical point of view.
Task 2: 1. Find bugs that could happen (logic errors, null reference risks, race conditions, and edge cases).
- Find security holes (risks of injection, bad validation, and unsafe data handling).
- Look into performance issues like loops that aren’t needed, operations that block, and queries that aren’t efficient.
- Look at problems with the quality of the code, such as SOLID violations, duplication, bad naming, and high coupling.
- When appropriate, suggest fixed code snippets.
- Tell me why each problem is a problem.
Be clear and give directions. Give better code examples when needed.
Diff for the Pull Request:
——————-
[PASTE DIFF HERE]
——————-
Step 3: Make suggestions for better refactoring
For the last step, let’s ask Copilot to suggest refactorings and other ways to make your code better while keeping performance, design patterns, and finding project pattern violations in mind. Some people might say that this step isn’t necessary because most of it should have been covered in the steps before it, but it’s always better to be safe than sorry.
You’re on the third step of an AI-assisted code review in a GitHub Copilot code review workflow.
The pull request diff below has already been summarized and checked for bugs.
Task: Suggest ways to improve refactoring that follow SOLID principles.
In particular:
- Find violations of:
– The Single Responsibility Principle (SRP)
– Principle of Open and Closed (OCP)
– Liskov Substitution Principle (LSP) – The Interface Segregation Principle (ISP)
– The Dependency Inversion Principle (DIP)
- Highlight tightly coupled components or areas with low cohesion.
3. Suggest structural improvements to improve maintainability and extensibility.
4. Recommend design patterns if applicable (Strategy, Factory, Adapter, etc.).
5. Provide improved code snippets where appropriate.
6. Explain why each refactoring improves long-term scalability and code quality.
Focus on pragmatic improvements suitable for production environments.
Avoid unnecessary over-engineering.
Pull Request Diff:
——————-
[PASTE DIFF HERE]
——————-
Real Code Example: Using Copilot for AI-Assisted Code Review
Now, here’s a real code example on how to use GitHub Copilot for AI-assisted code review and, in this case, refactoring as well. First, we will understand a code that isn’t very readable or well-made: though it works, it can be improved for better scalability, to prevent future bugs, and to remove “magic” numbers. Then we will run it through a prompt on Copilot and understand the resulting code. The order goes as follows:
Problematic code:
export function calculateDiscount(price: number, userType: string) {
if (userType === "premium") {
return price * 0.8;
} else if (userType === "vip") {
return price * 0.7;
} else {
return price;
}
}Used prompt:
Review this function and suggest improvements for scalability and maintainability.
Expected response:
- Suggestion for creating an enum
- Map strategy
- Better typing
- Possible validation
Refactored code:
enum UserType {
Premium = "premium",
VIP = "vip",
Standard = "standard"
}
const discountMap: Record<UserType, number> = {
[UserType.Premium]: 0.2,
[UserType.VIP]: 0.3,
[UserType.Standard]: 0
};
export function calculateDiscount(price: number, userType: UserType): number {
return price * (1 - discountMap[userType]);
}Though it seems a bit more confusing at first sight, as the code isn’t as obvious as before, it uses a mapping pattern, enums, has well-defined types, and is overall much more scalable and future-proof. Also, you can reuse the types you’ve created in other parts of the codebase, ensuring integrity, code reuse, and the adoption of clean code patterns.
Common Pitfalls of AI-Assisted Code Review
Even though tools such as Copilot are very powerful for coding workflow optimization and can be amazing pair programming partners, there are still some pitfalls that one might fall into, and making such mistakes can be a disaster. So, we have to warn you about the common points in which AI fails and can’t work very well, and the mistakes it usually makes (and probably will always make in the future as well). Some of these, on the other hand, are purely human-made, though they are AI-induced, and that’s even more dangerous: no AI can prevent the human operator from erring, so they should be well aware of the dangers.
Over-reliance
It happens when you rely too much on your AI coding assistant. You always need to double-check and perform heavy testing on everything that is automatically generated: agents hallucinate more often than not, and sometimes will very confidently give you code that simply does not work. So you should never blindly trust and always review what’s done as well.
False positives
An artificial intelligence agent will very frequently commit terrible mistakes with absolute confidence. It won’t admit it’s wrong, or very rarely so, and that is very misleading. We tend to believe what the chatbot is saying, as we more or less respect its training data and how incisive and confident it sounds, and end up trusting it too much.
All of this leads us to the conclusion that AI-assisted coding can never fully replace the human eye and a human operator. In the end of the day, everything still depends on humans actually understanding code, product concepts, architecture, design patterns, etc. You will always have to understand how your system is interconnected, what your code should and should never do, and finally, know how to guide your AI agent to the best result.
Conclusion: Building Smarter Code Review Processes with AI
To sum everything up, the AI takeover of development processes isn’t a fad or a mere current trend. It has come to stay and has already revolutionized the whole industry. AI tools for software development are more and more powerful, and can sometimes surpass a human developer by a lot. So, it is always a great addition to have alongside you, making a team, or using it as a copilot, as the name suggests.
With this, we can understand why companies that adopt AI-assisted coding and AI-powered software development have been performing so well. It seriously makes your development much faster, safer, and more reliable, as AI assistants are quickly becoming more specialized and trustworthy, and very quickly, they’ve become a staple in the industry. So, if you’re a developer seeking to stand out or a company looking to be up with modern trends, then you definitely should give it a try and see how much faster you can deliver and how good your code can become.
