👀 Code Reviews Reimagined: Leveraging AI for Startup Success
How AI can transform code reviews to benefit your startup;
Hello guys; I apologize for not writing consistently in this newsletter. Working in startups can be more than a full-time job, and it often absorbs us; we promise to write constantly and with better topics for our audience.
Welcome to our latest newsletter, where we'll discuss how AI can transform code reviews to benefit your startup; Code reviews play a crucial role in ensuring software quality. They help to ensure that code is functional, maintainable, and meets company standards. However, managing code reviews can be overwhelming for early-stage startups with limited resources. This is where Artificial Intelligence (AI) comes in - a revolutionary solution that can streamline and improve the code review process.
👁 What are code reviews, and why are they important?
Code reviews involve a developer examining another developer's code to identify errors, ensure adherence to coding standards, and encourage collaboration. These reviews are essential for catching bugs early, maintaining code quality, and promoting knowledge sharing among team members.
Early-stage startups frequently encounter challenges such as tight deadlines, limited staff, and speedy iteration requirements. These limitations make managing the code review process even more critical but challenging. We are four people at my workplace, making time a valuable resource. Therefore, we aim to make changes daily to satisfy our customers and demonstrate continuous improvement.
The traditional way of code review can be time-consuming and inconsistent, leading to delays and potential mistakes. The lack of standardized procedures and tools can further complicate the process. Scope switching can also be challenging for many individuals, resulting in some PRs waiting for review for an extended period.
👇The Current State of Code Reviews
Manual code reviews are essential but time-consuming and susceptible to human mistakes. Finding the necessary time and expertise to conduct these reviews in a startup setting may not always be easy. This can result in rushed or incomplete studies, which can negatively impact the quality of the final product. Balancing the need for speed with the desire for quality is a continuous challenge many startups face.
Another situation is how we assign code reviews. Typically, people try to give the review to someone with context on the code change. For example, if you change the "Notifications" module, you may assign the pull request to someone who has worked on or led that module. This is done because it will make for an easier review, or the selected person has the necessary context to make informed decisions about that code. While this can be true most of the time, it can lead to context lock, where only one person has all the knowledge for that part of the code, which is more harmful than good for your startup.
For those situations, I recommend using the round-robin algorithm. This will ensure that different people are assigned to different pull requests whenever the feature is available on GitHub.
⿰ The Importance of Descriptions and Self-Reviews
When submitting a pull request, the description guides reviewers through the changes. It's not simply a formality, but rather a roadmap that provides context explains the reasoning behind the changes, and outlines the solutions that have been implemented. A clear and well-written description helps reviewers better understand the "why" and "how" of the changes, resulting in a more efficient and insightful review process.
Writing a PR description may seem like a waste of time, but summarizing the changes to your peer will speed up the review process.
One way to help people summarize changes made to a Pull request is by using a PR template in your code. A blank page can be daunting when trying to describe modifications. GitHub offers this feature, and other products like Gitlab likely have something similar.
🧘The Role of Self-Reviews
The importance of self-reviews is undeniable. Before submitting code for review, it is crucial to perform a comprehensive self-review to detect potential problems or discrepancies. This practice can not only save time but also enhance the overall quality of the final product. Moreover, it can pinpoint areas where the review process can be simplified by catching issues beforehand rather than waiting for reviewers to bring them up.
In addition, self-reviews can improve your skills as a developer. Reviewing your code allows you to identify areas where you can improve and learn from your mistakes. It's an excellent opportunity to reflect on your work and grow as a developer.
The way that I do this is by creating a draft PR and then switching to review mode. I review my code as if it was not mine, which can sometimes be challenging. I leave comments and even suggest some commit changes. If there are any necessary changes, I make them locally and push them again; once I feel comfortable, I will request a review form from one of my peers.
🤖 AI's Role in Transforming Code Reviews
Ok, but regarding the use of AI in code review.?, You may already be aware of other coding tools available in the market, such as Github Copilot.
Code review tools powered by AI can automate the process of reviewing code and detecting issues accurately and quickly. These tools can analyze code based on predefined standards, offer immediate feedback, and even propose enhancements to the code.
Recently I found this tool:
PR-Agent This tool is open-source and designed to assist developers in reviewing pull requests with greater speed and efficiency.
As I mentioned earlier, our team at Prefix consists of only four people, and we often spend a significant amount of time reviewing code. Therefore, I suggested trying out a new tool to improve our process. After using it for a few weeks, I wanted to provide an honest review of our experience with the tool.
First, I want to mention how easy it is to set up this in your current codebase. There are several ways to install this, but I love the Run as a GitHub Action. This will take 5 minutes to create a PR with this GitHub action and merge it to your main branch, and you are ready to go.
Let's talk about some feature that PR-Agent provide;
/describe
my favorite so far is the /describe command; If you type your comment, the Agent can generate a description and title for your PR using the code changes. When I first tried it, I was surprised by the generated description.
/review
The PR review will provide feedback that can be adjusted based on the main theme, type, relevant tests, security issues, focus, score, and various suggestions for the PR content.
/improve
Improve provides commitable code suggestions to enhance the PR. Honestly, some of the suggestions may not be the best, but I firmly believe that with the passionate community working on this project, we will see rapid improvements in this feature over time.
Conclusions
I don't want to spoil the tool for you. Instead, I recommend you try it out and check the GitHub project. They have some cool things planned on their roadmap.
The potential of AI to revolutionize code reviews in early-stage startups cannot be denied. By utilizing AI, startups can overcome traditional obstacles, improve efficiency, and uphold high-quality standards. The future of code reviews has arrived, fueled by AI. If startups want to innovate and thrive, they should embrace this thrilling frontier.
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