GitHub Copilot - code review assistant
Pull Requests (PR) are a central part of many teams’ workflows. PRs allow us to consolidate changes into a single, manageable unit that can be communicated, discussed, and refined. But this process can be time-consuming.
Because of this, I’ve been exploring GitHub copilot code review as a complement to the traditional code review process to reduce this overhead. GitHub Copilot offloads basic reviews to a copilot agent that catches issues like logic bugs, potential performance problems, anti-patterns, and even suggests fixes to improve code quality.
When I first started using copilot, I primarily used it in my code editor for autocomplete and code suggestions. As copilot has grown and added new features, I have also expanded my use cases beyond my code editor and into other aspects of my day-to-day work, such as code reviews.
In this post, I will share my GitHub Copilot code review use cases and thoughts on the tool.
The Evolution of Code Review
Traditionally, the main purpose of code review was bug finding — reviewers were expected to act as quality gates to catch errors before production. Over time, however, the role of reviews has shifted. Teams began to use PRs not just to catch bugs, but also to share knowledge, enforce design consistency, and build collective ownership of the codebase. This shift means reviewers are now expected to focus on higher-level aspects of code: intent, maintainability, and architecture. The challenge is that low-level issues still consume time and attention, creating bottlenecks. This is where automation — and now AI — can play an important role.
What Is AI Code Review?
It is an automated code assessment process that examines the code of a software application for potential problems and inefficiencies. It involves the use of machine learning models to identify and fix coding errors, optimize code performance, and make recommendations for improvements.
Using GitHub Copilot to Review New Pull Requests
GitHub Copilot providing code review suggestions in the development environment
Before asking a teammate to spend their time on a pull request, I assign GitHub Copilot to perform an initial pass. Copilot quickly scans the changes, surfaces obvious bugs, suggests concise refactors, and drafts a clear pull-request summary that I can refine.
Using GitHub Copilot to Explain unfamiliar or complex code
GitHub Copilot providing detailed explanations of unfamiliar or complex code blocks
When I encounter unclear or cryptic code, I simply highlight the code block and ask GitHub Copilot to generate explanations to demystify its purpose. I simply highlight the code block and click the Copilot icon and prompt it to explain with:
> “Explain what this function does.”
> “Summarize this code for me.”
> “What is the purpose of this block?”
To understand unfamiliar or complex code quickly, I highlight the snippet and ask Copilot, “What is this doing?” or request a high‑level summary, a step‑by‑step walkthrough, or a short list of key variables and side effects. To keep the results reliable, I prompt precisely e.g.,
> “Give a one‑sentence summary, then three risks”
cross‑check the output against the source, and use the explanation as a starting point for targeted tests or follow‑up questions.
By providing concise explanations, copilot helps give me a clearer understanding of what the code is doing and why.
Using GitHub Copilot suggestions when reviewing code
There have been times I have had a deadline to complete a task and code review, I don’t always have the time to check every detail manually. That’s where I lean on GitHub copilot. That’s where I will ask GitHub Copilot to help me evaluate different aspects of the pull request. For example, I’ll sometimes ask copilot to double-check if the code is syntactically correct or if it follows standard best practices. While I can’t upload our internal company standards directly, I still compare copilot’s suggestions against what I know of our conventions. One recent case involved reviewing some Java code, where I noticed a potential null reference issue. Instead of writing out a full explanation, I asked copilot to analyze the snippet and confirm if my concern was valid. Its response highlighted the exact risk I had spotted and suggested a safer approach, which made my review more precise and actionable.
Another example was when a developer instantiated a new HttpClient directly in multiple methods. I suspected this could lead to resource leaks and performance issues, so I asked copilot for alternatives. It recommended using a HttpClient built through HttpClient.newBuilder() as a shared resource or leveraging a connection pool, which aligned with best practices in Java and reinforced my feedback.
Wrapping it Up!
Simply writing down code is only half to complete the job. The real test comes in reviewing it properly and catching potential issues before they can slip into production. That’s where GitHub Copilot has been a game-changer for me.
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