AI Code Reviewer
Code Quality
Amartya Jha
• 09 October 2024
Imagine you're a developer managing tight deadlines, complex projects, and constant changes. In the middle of all this, there's one task that always takes up your time: code reviews. As the founder of CodeAnt AI, I've spoken with over 100 engineering leaders in recent months, and they've all felt the same way—code reviews take a lot of time, are inefficient, and often slow things down in the development process.
Understanding the Purpose Behind Code Changes
When a developer submits new code, the first problem for the reviewer is to understand why these changes were made:
Going Through Large Code Submissions: Developers often submit big chunks of code, especially in fast-moving teams. This makes it hard to see what's important and why certain changes were made.
Connecting Code to Business Needs: Reviewers need to check tools like Jira to make sure the code matches what the business needs, which can be a boring and time-consuming task.
Time-Consuming Process: Just understanding the purpose of the code can take anywhere from 5 to 15 minutes per pull request.
Spotting Quality Issues
After figuring out the "why," reviewers need to look for ways to improve the code and explain their suggestions clearly, like:
Suggesting Better Data Structures: Recommending more efficient ways to write the code.
Ensuring Proper Error Handling and Formatting: Making sure the code handles errors properly and follows style guidelines.
Proposing Enhancements: Offering ideas to make the code run faster or be easier to read.
This step can take an additional 15 to 20 minutes as the reviewer carefully reviews the code and provides helpful feedback.
Identifying Security Problems
Checking for security issues is very important but often missed because of several reasons:
Limited Security Knowledge: Developers may not be experts in security, so they might miss important vulnerabilities.
Common Mistakes: Accidentally leaving passwords or API keys in the code, known as exposed secrets, can cause serious problems.
Risks with Third-Party Code: Using open-source libraries can bring in hidden security problems.
Ineffective Tools: Even when tools exist to detect these issues, they don't always work well or are not well connected to the way developers work.
Reviewing for security can take 5 to 10 minutes, but it's essential to prevent serious problems in the future.
Ensuring Compliance with Standards
Finally, reviewers must make sure the code follows the required rules and laws:
Complex Compliance Requirements: Rules like SOC 2, ISO, GDPR or HIPAA can be complicated, and developers might not know about them.
Delayed Feedback: Often, compliance teams identify issues after the code is written, leading to doing the work again.
Development Delays: This going back and forth not only slows down the process but also makes developers frustrated.
Think about it this way. A careful code review can take 25 to 40 minutes per pull request. Now, imagine a team of 100 developers:
Average Code Changes: Each developer makes one code change every two days.
Monthly Pull Requests: That's about 1,500 pull requests in a month.
Total Time Spent: At 25 minutes per pull request, that's 37,500 minutes, or about 26 full workdays spent on code reviews each month.
This isn't just a number in theory—we've seen it happen with our customers, especially those at Series C and above. For more details, check out our customer case studies.
As development teams grow, these problems increase. Code reviews become a major bottleneck, causing delays and increasing costs. So, how can we make code reviews faster and more effective without lowering the quality?
Rapidly Understanding Code Changes
AI can help reviewers quickly understand why the code was changed:
Summarizing Changes: AI provides a short summary of what the new code does, saving time.
Highlighting Key Differences: It points out important changes from the previous version, so reviewers know where to focus.
Linking to Business Objectives: AI connects code changes directly to business goals or user stories, making the purpose clearer.
Instantly Identifying Critical Issues
AI tools can automatically detect:
Quality Issues: Showing where the code can be improved and providing clear, understandable comments for developers.
Security Vulnerabilities: Finding exposed secrets, insecure code patterns, or risky dependencies that might be missed.
Compliance Violations: Checking if the code meets required standards and regulations, alerting developers immediately.
With AI, developers and reviewers can see the biggest problems right away, all in one place.
Enforcing Custom Company Policies
Every company has its own coding standards and best practices. Traditional tools can be inflexible and difficult to change. AI offers a solution:
Learning Your Company's Guidelines: AI understands and enforces your specific naming conventions, code structures, and style guidelines.
Adapting Over Time: As it learns from your codebase, AI gets better at enforcing policies and finding differences.
Simplifying Policy Management: Making it easier to manage policies, ensuring everyone follows the same rules.
Providing Immediate Feedback
Adding AI into the code review process means:
Seeing the Effects Right Away: Developers can instantly see how their changes affect the entire system, including upstream and downstream effects.
Instant Alerts: They receive alerts about security, quality, or compliance issues as soon as they submit code.
Fixing Problems Early: Early detection allows issues to be fixed right away, preventing bigger problems later on.
Using AI for code reviews offers several benefits:
Time Savings: Reduce the time spent on understanding and reviewing code, freeing up developers to focus on building features.
Enhanced Code Quality and Security: Catch more issues automatically, leading to more robust and secure software.
Effortless Compliance: Keep code following all required rules without extra effort.
Increased Productivity: Make the development process smoother, allowing teams to deliver faster.
Using AI in code reviews is a major improvement for software development. At CodeAnt, we're leading this change, helping teams review code faster. By providing immediate insights into code changes, highlighting critical issues, and ensuring compliance, we're changing the way developers work for the better.
With the big time savings—potentially reclaiming 26 full workdays per month for a 100-developer team—the impact is huge. This isn't just about saving time; it's about empowering developers to focus on innovation and creating great software.
As AI keeps improving, adding it to the development process will become more important. We're excited to be part of this journey and look forward to making coding easier and more enjoyable for everyone.
