AI code generation tools are getting better and better every month, helping engineers build software faster than ever before. However, AI code generation also poses new challenges, especially when it comes to maintaining code quality. As AI coding tools become ubiquitous, the sheer volume of code produced can overwhelm even the best engineering teams. What if we could use AI to help tackle this increased volume of code changes? By leveraging an AI-powered review tool to assist in reviewing AI-generated code, engineering teams can streamline review cycles, boost developer productivity, and ensure that quality doesn’t slip through the cracks as they adopt these powerful new AI tools. In this blog post we’ll explore how to get the most out of AI-powered code review tools to reduce your team’s review cycles and improve developer productivity.
The challenges with traditional code reviews
If you ask any developer what the most challenging part of the code review process is, they’ll likely say waiting for reviews. Reviewers are often juggling multiple tasks, leaving pull requests (PRs) in limbo for hours, days, or even years. This delay can cause a domino effect: without stacking, change authors are blocked from working on the same part of the codebase while they wait for feedback, bringing the project to a complete halt. As teams scale and codebases become more complex, these inefficiencies only become more pronounced.
Additionally, human error and bias can further complicate the process. Reviewers may overlook bugs or prioritize certain aspects of the code change, leaving other sections untouched. This oversight can inadvertently allow problematic code to pass through to production, causing regressions and impacting users. Gaps in communication can also create open feedback loops—PRs can go unnoticed, comments might be missed, or feedback may be delayed, all of which slows the development cycle. All of these issues are only amplified by AI code generation tools - more code changes from each developer also means more PRs that need to be reviewed.
This is where AI can help ensure that code reviews are not only faster, but also more consistent and thorough. By automating the repetitive tasks, AI helps reduce bottlenecks, eliminates bias, and ensures that no PR falls through the cracks, resulting in quicker and more effective code reviews.
How AI reduces review times
One area that AI review tools excel in is cutting down on time to first review. AI can provide near-instantaneous feedback on published pull requests, allowing authors to address issues in real time without waiting for a human reviewer to become available. This immediate feedback loop accelerates the entire review process, helping developers iterate on their code more quickly and merge PRs faster.
In addition to reducing time to first review, AI can also help by automating other repetitive tasks in the review cycle:
Automated bug detection: AI can instantly identify common coding mistakes and bugs, saving hours of back-and-forth corrections.
Security vulnerability identification: AI can detect security vulnerabilities, such as weak encryption, improper authentication, or insecure dependencies, long before they pose a threat to end users.
Performance issue detection: AI can also flag functional inefficiencies that can lead to performance bottlenecks and a bad user experience.
Through automating these critical checks, AI code review can help engineering teams move through the review cycle more efficiently and allows developers to prioritize what matters most: building great software.
Improving developer productivity with AI code reviews
Not only does AI speed up the review process—it unlocks developers' potential by giving them time back to focus on high-priority work.
More time for high-impact work
According to the State of code review 2024, the median engineer at a large company takes around 13 hours to merge a pull request, spending a majority of this time waiting on code review. Instead of spending hours going back-and-forth with reviewers on the PR, they could reclaim this time and start building the next most important feature or refining the user expeirence.
Reduced cognitive load and toil
Manual code reviews can also be mentally exhausting, especially when developers are juggling multiple projects. GitHub’s research on AI as a "second brain" highlights how AI tools can take over repetitive, low-value tasks, reducing the mental strain on developers. With AI assisting in catching bugs and flagging other small issues, human reviewers no longer need to examine every little detail of a code change, allowing them to maintain sharper focus on creative problem-solving. For example, an AI code reviewer might flag a missing input validation before a human reviewer even sees the pull request, allowing the author of the code to quickly fix the issue, eliminating an entire review cycle.
Minimized context switching
Context switching is one of the biggest productivity killers for developers. Shifting focus between tasks—like jumping from writing code to reviewing a teammate's pull request—can lead to wasted time and mental fatigue. AI-driven code review tools help reduce this friction by minimizing the need for constant back-and-forth, allowing developers to stay focused on their core tasks. For instance, if a developer is deeply involved in a new feature but is pulled away to review a PR, AI can handle the boring & repetitive tasks—checking for bugs and best practices—so the reviewer can focus on the high-level architecture and functionality and get back to their own work faster.
Asynchronous workflows
An AI-powered approach is especially valuable for distributed teams, where time zone differences can create delays in the development process. With AI-driven code reviews, teams no longer need to wait for the next workday to begin in another region before getting feedback, allowing for continuous iteration even when the team is offline. An AI reviewer is like a co-worker who is “always online”, closing the gap between timezones, unblocking developers to continuously iterate on feature development no matter where they are geographically located.
Key takeaways
At the end of the day, AI developer tooling should enable your engineering teams to deliver high-quality code more efficiently. By incorporating AI into how you review code as well as how you write code, you reduce bottlenecks, eliminate unnecessary delays, and catch defects that human reviewers may otherwise miss. This empowers developers to focus on solving complex problems, rather than getting bogged down in the repetitive and time-consuming aspects of code reviews. As AI code generation becomes more and more powerful, engineering leaders at the world’s fastest-moving software companies are integrating AI code review tools into their development workflows to help their teams ship quickly and safely.
