Table of contents

• Graphite: AI-enhanced reviews and stacked PRs
• Aikido Security: Real-time code quality insights
• GitHub Copilot: AI-powered code suggestions
• SonarQube: Continuous code quality inspection
• Fine: AI-driven PR review assistant
• Comparison overview
• Conclusion
• FAQ

Reducing review latency—the time between when a pull request (PR) is submitted for review and when reviewers complete their assessment—is a critical challenge for engineering teams. Long review cycles create bottlenecks that block developers, slow feature delivery, and reduce team productivity. AI-powered code review tools have emerged as effective solutions to accelerate reviewer feedback by automating routine checks, surfacing critical issues, and making reviews more efficient. This guide will provide an overview of current leading AI code review tools to reduce review latency.

Graphite: AI-enhanced reviews and stacked PRs

Graphite is a code review platform designed to accelerate the review process by implementing stacked PRs—a method that breaks down large changes into smaller, more manageable units. This approach makes it easier and faster for reviewers to assess code, reducing the cognitive load and time required for each review.

Key features:

• Stacked PRs: Enables the creation of dependent PRs, allowing reviewers to assess incremental changes in isolation, making reviews faster and less overwhelming.
• AI code reviewer: Automates initial code assessments, providing instant feedback on style, syntax, and potential issues, reducing the workload on human reviewers.
• Graphite Chat: Integrates conversational AI within the PR interface, enabling reviewers to quickly ask questions and get clarifications without waiting for author responses.
• Smart notifications: Proactively notifies reviewers when PRs are ready for review, ensuring reviews aren't delayed due to lack of visibility.

Impact on review latency: By breaking large PRs into smaller, digestible units and automating routine checks, Graphite significantly reduces the time reviewers spend on each PR.

Aikido Security: Real-time code quality insights

Overview: Aikido Security focuses on providing real-time, actionable insights into code quality, emphasizing security and compliance to help reviewers quickly identify critical issues.

Key features:

• Semantic analysis: Evaluates code context to identify potential vulnerabilities and areas for improvement, highlighting priority issues for reviewers.
• Customizable rules: Allows teams to define project-specific guidelines and standards, reducing subjective back-and-forth during reviews.
• Integration with GitHub and GitLab: Seamlessly fits into existing workflows, providing instant feedback directly within the GitHub and GitLab version control platforms.

Impact on review latency: By automatically surfacing security and quality issues before human review, Aikido Security allows reviewers to focus on architectural and business logic concerns, reducing the time spent identifying common issues.

GitHub Copilot: AI-powered code suggestions

Overview: GitHub Copilot assists developers by providing AI-generated code suggestions within the integrated development environment (IDE), which indirectly impacts review speed by improving initial code quality.

Key features:

• Contextual code suggestions: Offers real-time code completions based on the current coding context, helping developers write cleaner, more consistent code.
• Multi-language support: Supports a wide range of programming languages and frameworks, ensuring consistency across the codebase.
• Learning from open source: Trained on a vast corpus of public code, promoting best practices that reviewers expect.

Impact on review latency: By helping developers write cleaner, more consistent code from the start, GitHub Copilot reduces the number of issues reviewers need to flag, allowing them to complete reviews faster with fewer revision cycles.

SonarQube: Continuous code quality inspection

Overview: SonarQube is an open-source platform that continuously inspects the code quality of projects, identifying bugs, vulnerabilities, and code smells before human reviewers see the code.

Key features:

• Static code analysis: Performs in-depth analysis of code to detect potential issues, providing reviewers with a pre-vetted list of concerns.
• Quality gates: Enforces standards by setting thresholds that code must meet before review, reducing the need for reviewers to check basic quality metrics.
• Multi-language support: Supports a wide array of programming languages, providing consistent quality checks across the entire codebase.

Impact on review latency: By automatically flagging issues in the CI pipeline before human review, SonarQube ensures reviewers only see code that meets basic quality standards, allowing them to focus on higher-level concerns and complete reviews more quickly.

Fine: AI-driven PR review assistant

Overview: Fine is an AI-powered PR review assistant that provides detailed, context-aware feedback to supplement human reviewers and accelerate the review process.

Key features:

• In-depth code analysis: Examines code for readability, maintainability, and adherence to best practices, giving reviewers a head start.
• Actionable feedback: Provides specific, prioritized suggestions for code enhancements, helping reviewers quickly assess what matters most.
• Integration with GitHub: Works directly within GitHub, allowing reviewers to see AI feedback alongside the code without context switching.

Impact on review latency: By providing comprehensive initial feedback, Fine allows human reviewers to validate AI suggestions rather than finding issues from scratch, significantly reducing the time spent on each review while maintaining thoroughness.

Comparison overview

Conclusion

Integrating AI-powered code review tools into your development workflow can dramatically reduce review latency by automating routine checks, surfacing critical issues instantly, and making code easier to assess. Graphite, with its stacked PR approach and AI-powered reviews, offers a comprehensive solution to accelerate reviewer throughput while maintaining quality. The key to reducing review latency is selecting tools that help reviewers work more efficiently—whether by breaking down complex changes, pre-vetting code quality, or providing actionable feedback. Combining multiple tools can create a review pipeline where human reviewers spend their time on what matters most: architecture, business logic, and design decisions.

FAQ

What is review latency and why does it matter?

Review latency is the time between when a pull request is submitted for review and when reviewers complete their assessment and provide feedback. High review latency creates bottlenecks that block developers from progressing, slows down development cycles, and can lead to context switching and reduced productivity. Reducing review latency ensures faster feedback loops while maintaining code quality.

How do AI code review tools reduce review latency?

AI code review tools reduce review latency by automating routine checks before human review, surfacing critical issues instantly, and making code easier to assess. They handle style violations, common bugs, and security vulnerabilities automatically, allowing human reviewers to focus on higher-level concerns and complete reviews faster. Tools like Graphite's stacked PRs also break down large changes into smaller units that reviewers can assess more quickly.

Which AI code review tool is best for reducing review latency?

The best tool depends on your team's specific review bottlenecks. Graphite excels at reducing review latency through stacked PRs, AI-powered reviews, and smart notifications. Fine provides comprehensive AI feedback that helps reviewers validate rather than discover issues. SonarQube ensures reviewers only see quality-vetted code. Consider where your review process slows down—is it large PRs, routine issue detection, or lack of reviewer availability?—and choose tools that address those specific challenges.