The Code Review Process Is Outdated — A Solution
The code review process is outdated and lagging behind the rapid improvements in other areas of software development.
Compare the process of reviewing code to the process of writing code.
The former stagnates, while the latter progresses at a lightning pace. Copilot, LLMs, Chatbots, AI code editors, you name it. All these tools help you massively with writing code. They simplify the process of writing code and increase your efficiency and code quality.
Yet the code review process remained largely the same. You are most likely reviewing code manually, one line at a time.
While this method is still necessary, it’s extremely slow, time-consuming, and error-prone.
In this article, you’ll learn why a change is greatly needed in this space and how you can improve the code review process.
Problems With the Traditional Code Review Process
Before going further, let’s explore the biggest issues with traditional code reviews, such as the time-consuming nature of manual reviews and the potential for human error.
A) Time-Consuming
Code review is one of the slowest parts of software development and it can sometimes be a bottleneck that slows down the process of shipping code.
The main reason why code review is so slow is that each team member needs to review the code line by line. The reviewer has to check for various aspects, such as:
- Code correctness: Does the code work as intended? Does it handle all the edge cases well? Does it return the appropriate result for each scenario?
- Code readability: Is the code understandable and comprehensible? Does it include descriptive variable and function names, proper formatting and indentation, and meaningful comments?
- Code performance: Is the code efficient, or is it just a brute-force solution? Can the code be optimized further? Will this code degrade the overall performance of the system?
- Code security — is the code secure enough to avoid potential vulnerabilities?
- Code scalability — will the new code handle increased workload without significant degradation in performance or functionality?
All this process involves going over the code line by line and testing it multiple times. As a result, you spend significant time just reviewing a single pull request. Now multiply that with a couple of pull requests each week.
The time you spend reviewing code can add up significantly, especially in larger teams or projects with frequent code changes. This affects not only individual team members like you but the team as a whole, resulting in less code shipped and slower progress overall.
While manual code review is valuable and necessary, there are tools you can use to streamline the process and minimize the burden on team members.
B) Prone to Human Error
As the previous section illustrates, you need to consider many aspects when reviewing code, which leads us to the next problem.
The next significant issue with manual code reviews is the potential for human error. Reviewing a pull request requires a great deal of mental energy. You must be fully focused to give proper, useful feedback and avoid overlooking potential issues.
Even when you give your absolute best, you’ll still overlook things. And it’s not just you. It happens to everyone, no matter how much experience one has. Even the most experienced developers overlook potential issues and make mistakes during the review process.
A code reviewer can:
- miss subtle bugs—it’s really easy to overlook small details or edge cases, especially when reviewing large and complex pull requests. What’s worse, you can miss even obvious ones due to various factors, such as fatigue and tight deadlines, for instance. This can lead to significant problems down the line when the code is deployed.
- overlook potential issues and security vulnerabilities—this can happen due to factors such as mental fatigue, lack of experience, and close deadlines.
- experience fatigue — reviewing code for extended periods of time can lead to mental fatigue. That impacts the reviewer’s attention to detail and increases the likelihood of making mistakes.
- treat some code reviews like a chore — sometimes, team members are pressured into reviewing a particular pull request, and they do it superficially. Although it’s unprofessional, it can happen from time to time.
All of this contributes to the potential for human error when reviewing code and increases the likelihood of bugs slipping through. This can lead to costly issues that damage the product’s reputation and deteriorate the user’s trust in your product.
While human error cannot be eliminated completely from the code review process, there are tools you can use to minimize its impact.
In the following section, you’ll see how you can leverage AI to automate certain aspects of code reviews. AI tools help you streamline the code review process, improve code quality, speed up code reviews, and minimize the number of issues that slip through.
Solution: Augment Code Reviews With AI
Thankfully, the code review space has started to evolve and catch up with the code writing space. AI-powered code review tools are now available, improving how we review code and addressing the limitations of traditional manual reviews.
These tools leverage artificial intelligence and machine learning to analyze code changes, identify potential issues, and provide valuable insights such as summaries and repository stats.
One such tool is CodeRabbit, an AI-powered code reviewer that automatically reviews your pull requests, provides context-aware feedback, and even suggests code changes.
Let’s look at what it can do and how it can make code reviews faster, more interactive and even more enjoyable for you.
- Pull Request Summaries
The first hurdle of code reviews is understanding the changes introduced by the pull request. You need to go over the changes, test them locally, and figure out the full scope of the changes.
This process can be time-consuming since it usually requires multiple iterations before fully understanding the pull request.
CodeRabbit eliminates this obstacle by providing comprehensive summaries of pull requests. Even better, it offers detailed walkthroughs of the pull requests by breaking down the changes introduced file by file (or by directory).
This feature enables you to grasp the changes quickly, saving time that would otherwise be spent understanding the changes introduced by the pull request.
Although manual review and testing are still necessary, the added context streamlines the code review process, making it more efficient and faster.
2. Chat with Code
CodeRabbit also offers conversational capabilities, allowing you to chat with the code.
You can interact with it by:
— asking it to explain why it suggested specific changes
— telling it to suggest another alternative
— correcting it when it’s wrong so it can learn and get better
Think of it as your experienced teammate with a huge amount of knowledge and unlimited availability. Chatting with the code makes the review process more engaging and enjoyable.
Moreover, the tool learns from your interactions, continuously enhancing its performance over time.
3. In-depth Code Reviews
CodeRabbit also provides incremental in-depth code reviews. That means it reviews your code after each commit.
We are prone to overlooking things and making mistakes as humans. So, it never hurts to have a “second pair of eyes” looking over the code.
That’s where CodeRabbit helps. It scans every line of code to identify potential issues, bugs, and vulnerabilities you might miss. The good thing is that it not only points out the issues but also provides explanations and actionable suggestions. As mentioned previously, you can consider it your coding buddy who guides you.
As the image shows, CodeRabbit detects the issue, explains it, and then suggests changes. You can even commit the proposed change by clicking the button in the “Committable suggestion” section.
This way, you’re less likely to miss issues and bugs that might slip through into production.
Conclusion
It’s important to mention once again that AI tools like CodeRabbit don’t replace traditional code reviews altogether. Instead, they augment them by providing assistance and automating certain tasks, allowing you and other code reviewers to focus on higher-level concerns and more complex issues.
CodeRabbit can be considered a first line of defense, catching common mistakes, identifying potential bugs, and suggesting context-aware improvements.
Combining CodeRabbit with your expertise improves the code review process by removing repetitive and time-consuming tasks, freeing you to focus on the more important parts of the review.
This collaborative approach leads to better code quality, reduced risk of bugs and vulnerabilities, and faster shipping.
Personally, I’ve been using CodeRabbit at work for a few months now (around 6 months), and I’m more than happy with it.
Code reviews are a lot faster and more enjoyable for me. I can understand other people’s code faster since I have the whole picture before starting the reviews. Also, I can converse with the tool and ask it to explain parts of the code that I don’t understand. This has made the review process more interactive and engaging. So, CodeRabbit became an essential tool in my toolbox. It has transformed code reviews from a tedious task into a valuable learning experience.
Give it a try!
If you want to learn how CodeRabbit works, I encourage you to read the “Designing CodeRabbit” section on their website.
CodeRabbit has a 7-day free trial for private repositories, and it’s free for open-source projects. Thanks to Code Rabbit for sponsoring my post.
