Best 6 AI Apps Developers Use for Reviewing Pull Requests
Artificial Intelligence has quickly carved out its place in software development workflows, offering automation, efficiency, and accuracy—especially in code review processes. Developers increasingly rely on intelligent tools to streamline time-consuming tasks like reviewing pull requests (PRs), ensuring quality assurance, and identifying critical bugs long before they reach production.
TL;DR
AI-powered tools for reviewing pull requests have become essential in modern development. They assist in catching errors, suggesting improvements, and significantly speeding up the review process. Six standout apps developers trust include GitHub Copilot, CodeWhisperer, DeepCode by Snyk, Codacy, Reviewpad, and PullReview. Integrating one or more of these tools can vastly improve code quality and reduce developer fatigue.
Why AI for Pull Request Reviews?
Code reviews are vital for maintaining codebase quality, but they can be tedious and time-consuming, especially as teams and projects grow. AI helps alleviate that burden by:
- Detecting bugs and vulnerabilities before human reviewers even look at the code
- Suggesting improvements in logic, syntax, and structure
- Ensuring consistency with linting and style guides
- Reducing reviewer workload by highlighting only significant changes or anomalies
Let’s explore the six best AI tools developers are using for PR reviews in 2024.
1. GitHub Copilot
Developed by GitHub and OpenAI, GitHub Copilot has emerged as one of the leading AI tools for coding assistance, but it’s also making waves in the pull request review space.
Copilot can intelligently suggest code revisions and provide real-time context-aware assistance by learning from billions of lines of code. When reviewing a PR, it can:
- Suggest better variable names or function logic
- Identify redundant code or potential errors
- Provide natural language summaries of a PR change
With its tight integration in the GitHub ecosystem, it’s especially handy for teams already working within GitHub repositories.
Image not found in postmeta2. Amazon CodeWhisperer
Amazon CodeWhisperer is AWS’s answer to AI-driven code suggestions and reviews. Like Copilot, it leverages deep learning to offer smart autocompletions and code fixes.
What sets CodeWhisperer apart is its built-in awareness of AWS libraries and services. During PR review, it excels at:
- Auditing cloud-related code for security best practices
- Recommending changes that are optimized for AWS deployments
- Flagging potentially dangerous patterns in serverless and IAM configurations
For development teams heavily invested in AWS, this tool improves not just code quality but deployment security, too.
3. DeepCode by Snyk
DeepCode uses AI to provide deep semantic code analysis. It’s especially adept at finding vulnerabilities and logic errors before they cause trouble in production.
With support for over 15 programming languages and seamless integration into modern CI/CD pipelines, DeepCode offers:
- Real-time analysis of pull requests
- Detection of security vulnerabilities early in the review phase
- Feedback grounded in real-world code examples and best practices
DeepCode merges the powers of AI and decades of developer knowledge, providing feedback that feels almost like a seasoned engineer reading your PR.
Image not found in postmeta4. Codacy
Codacy is a versatile AI-powered code quality tool that automates PR review with a focus on maintainability, complexity, and standards compliance.
Though it may not be a code-completion tool like Copilot, Codacy is incredibly powerful for teams that want to:
- Ensure consistent styling and adherence to custom coding standards (supports over 40 languages)
- Automatically score pull requests based on code quality and risk level
- Generate metrics and historical performance data for each contributor
Codacy is both a static analysis and performance measurement tool, making it great for long-term codebase health monitoring.
5. Reviewpad
Built specifically for pull request reviews, Reviewpad applies AI in a very targeted manner to simplify the PR workflow.
Reviewpad shines with features such as:
- Automatic labeling and categorization of pull requests
- AI-generated reviews that flag potential problems, typos, and complex diffs
- Configurable workflows using Reviewpad’s scripting language for customized PR policies
It’s a young contender but is growing fast in popularity, especially for remote and agile teams managing multiple repositories across services like GitHub and GitLab.
6. PullReview
PullReview is one of the lesser-known tools but has a glowing reputation among developers who want deeply customizable and maintainable code reviews.
Its biggest strengths include:
- Machine learning models trained specifically for refactoring and design principles
- Integrations that deliver review feedback as easy-to-read dashboards
- Custom rule definitions allowing dev teams to tailor the feedback completely
Its level of review detail means PullReview is an ideal choice for enterprises and large teams with rigid quality gates and standards.
How to Choose the Right AI Reviewer for Your Team
Choosing from these AI tools depends on a few critical factors:
- Language support: Ensure the tool supports your stack. Some tools perform better on Python or JavaScript, while others thrive on Java or TypeScript.
- Tool ecosystem integration: GitHub Copilot works great inside GitHub. Amazon CodeWhisperer is a natural fit for AWS users.
- Security vs. productivity: If your codebase handles sensitive data, DeepCode and Codacy’s secure code review features will be invaluable. For speed, Copilot and Reviewpad are excellent choices.
- Pricing and scalability: Freemium models are helpful for small teams. Enterprise-level packages come with SLA guarantees, more customization, and scalability features.
Final Thoughts
The future of coding undoubtedly includes a strong partnership between developers and AI assistants. Tools that help streamline repetitive tasks—like reviewing pull requests—not only save precious developer time but also improve code consistency, security, and reliability.
As these tools become more sophisticated, we can expect fewer bugs slipping through the cracks, quicker turnaround during code reviews, and even better onboarding experiences for junior developers.
Whether you’re managing a multi-team microservices architecture or simply want better PR hygiene in your side projects, there’s an AI review tool built for your needs. Incorporate one—or a combination—of these six into your workflow and take your pull request game to the next level.
