8 AI Code Review Tools Developers Should Know in 2025

What is AI Code Review?

It uses artificial intelligence techniques and tools to review code for functionality, style, and quality. The automated methodology uses machine learning to find mistakes in coding standards and identify security vulnerabilities and issues.

It often offers automated fixes and guidance to developers. AI code review tools can save you time and enhance code quality. Integrated into version control systems and development environments, they boost continuous integration and delivery (CI/CD) practices. Here's a breakdown of the fundamental segments:
 

1. Code Analysis

• Static Analysis: AI tools check the code without executing it. They look for structural problems, security weaknesses, code smells, and signs of deeper issues.
• Dynamic Analysis: Artificial intelligence can run code in a controlled setting to review how it works and performs. This feature isn't common in most basic AI-powered code review tools. 

2. AI Techniques & Technologies

• Natural Language Processing (NLP): It uses NLP to understand the purpose of the code, identify patterns and issues, and learn from past data of previously analyzed code. 
• Machine Learning (ML): Machine learning algorithms analyze large sets of existing code to find bugs, inefficiencies, and other potential problems. 
• Large Language Models (LLMs): You can use LLMs to give helpful suggestions and recommendations for fixes, improvements, and optimizations based on the context.

Top Benefits and Concerns of AI Code Review Tools

Code review software transforms how developers analyze and improve code, offering faster feedback and higher accuracy. However, while these tools bring efficiency and automation, they raise concerns about reliability, security, and human oversight.

Let's explore the key benefits and challenges of integrating AI into the code review approach.

Benefits

• Speed and Efficiency: It can quickly scan large codebases and suggest improvements, reducing review time.
• Consistent and Accurate Analysis: Unlike humans, artificial intelligence doesn't suffer from fatigue or bias. It analyzes code consistently and accurately.
• Detection of Hard-to-Find Errors: AI excels at spotting subtle errors or bugs that are difficult for human reviewers to catch, especially those that only appear under specific conditions.
• Enhanced Developer Learning: It provides instant feedback, helping developers learn new coding techniques and improve their skills.

Challenges

• Over-Reliance on AI: Dependence on code review software may lead developers to neglect their judgment. This can hinder their understanding of code and complicate debugging.
• Lack of Context and Intent Understanding: It can't grasp the developer's intent behind the code, potentially flagging code that's correct or missing issues that a human would notice.
• False Positives and Negatives: AI code reviewers can generate false positives or negatives, leading to unnecessary work or missed bugs and vulnerabilities.

The global AI Code Tools market was worth $6.7 billion in 2024 and is expected to reach $25.7 billion by 2030, with an annual growth rate of 25.2% from 2024 to 2030. This report examines market trends, reasons for growth, and future forecasts, helping you make informed business decisions.

These tools are revolutionizing software development by automating mundane tasks, enhancing code quality, and speeding up project schedules. It leverages refined machine learning algorithms to help developers write, debug, and improve code.

Like those found in widely used IDEs, code assistants powered by artificial intelligence offer instant suggestions, auto-completion features, and error corrections. This helps minimize the time dedicated to routine coding activities and allows developers to concentrate on addressing more complex challenges.
 

The Best AI Code Review Tools

1. Kodesage

Kodesage can connect with your entire codebase and work with Git and GitHub repositories. It's invaluable for dealing with old code, reviewing outdated technologies, and assisting teams in modernizing their projects.

Kodesage creates a dynamic knowledge base from all connected sources (codebase, issue tickets, database schemas, wikis, tests and other sources). It makes this knowledge queryable using large language models (LLMs) through the Ask Kodesage feature. Both engineering and non-technical teams can ask questions about systems and receive suggestions for adding new features. Customizable prompt templates let Kodesage AI act like a senior engineer, which can also help to speed up the onboarding of new team members.

The issue analysis feature analyzes issue ticket (e.g. Jira), and provides automated fix recommendations. The auto -commenting capability, when turned on, can leave fix recommendations directly in the connected ticketing system, in specific tickets.

Key Features:

• Deep Code Intelligence: language agnostic parsing with specialized support for legacy technologies including Oracle Forms, PL/SQL, AS/400, Informix 4GL, COBOL, PowerBuilder, Delphi, and SAP. It can also handle modern languages.
• Issue analysis: integrates with issue ticketing systems (like Jira or Redmine), and generates fix recommendations for tickets. It also comes with an auto-commenting feature, that adds fix suggestions directly in tickets.
• Ask Kodesage: a natural language chat interface that both developers or other team members can ask questions about a system like "how does a specific function work", "how it can be updated", or "Where is this bug coming from?".  It also maps dependencies or relationships, and can visualize these with mermaid diagrams. 
• Customizable Prompt templates: Developers can provide more specific instructions or prompts to guide the AI, making it adaptable to different coding needs and environments. It can also accelerate the ramp-up cycle of new or junior developers, by guiding them through all crucial steps to be ready for their first push to production.
• Automated documentation: It can generate comprehensive documentation for systems, by extracting business logic from the source code and other integrated sources. It comes with a pre-built template library, being able to write documents in the required standard. It is connected to the codebase, and will keep documents up to date as a project evolves.
• Secure on-premise deployment: Kodesage is designed to be deployed on-premise, and can even run in air-gapped environments.

Pros & Cons:

2. Codacy

This innovative, free code explainer AI evaluates your code quality in real time. It offers invaluable insights and actionable suggestions for enhancement. With Codacy, DevOps teams can effortlessly automate their code reviews, streamlining the entire process.

Key Features:

• Offering smooth integration with Jira, Bitbucket, Slack, and GitHub.
• This tool helps review code automatically for 30+ programming languages.
• An AI code reviewer who assists in monitoring progress, evaluating commits, and safeguarding the codebase in Git.

Pros & Cons: 

3. CodeClimate 

Introducing an AI code analyzer designed to elevate your coding experience. This tool deepens into code complexity, duplication, and style violations, offering invaluable insights.

Key Features:

• CodeClimate helps you check test coverage for each line when you review code changes.
• It also gives automatic feedback on your pull requests so you can stop merging code without enough tests.
• Tackle technical debt head-on. Fast find files that change often but don't have adequate coverage.
• Track your progress with measurable goals, monitoring your code's health day by day.

Pros & Cons:

4. SonarQube Community Edition 

Discover the power of a free, open-source alternative to SonarQube. This platform is designed for continuous code quality inspection, ensuring your projects are always at their best.

Key Features:

• It finds bugs, security issues, and weak spots in different programming languages.
• This code review software identifies code that breaks best practices, making it easier to read and maintain.
• It sets code quality standards to ensure new code changes don't cause problems. If these standards aren't met, it'll fail the pipelines.
• This tool automatically uses different DevOps platforms to analyze code during the build process.
• Get useful Clean Code metrics within minutes

Pros & Cons:

5. Snyk 

This AI code review tool specializes in pinpointing bugs in open-source code libraries. It connects with different development platforms and tools to analyze code throughout the process and in continuous integration channels.

Key Features:

• Intelligent code creation integrated into your IDE.
• Code scanning in real-time.
• Recommended solutions for easy vulnerability resolutions.
• Open source helps identify and resolve vulnerabilities in your code review software.

Pros & Cons:

6. PullRequest 

This platform provides AI-powered code review services. It connects you with experienced developers who'll check your pull requests. The service uses AI technology to find code quality and security issues.

Key Features:

• It makes development faster by using AI and human skills to find code problems.
• PullRequest not only finds problems but also helps improve code quality.
• An AI code review tool that connects developers with experienced industry experts in different fields. Developers can get valuable feedback from these experts.
• Its ongoing code review process helps you identify trends and patterns in your development workflow.

Pros & Cons:

7. GitHub Copilot 

This AI code reviewer provides coding assistance. It acts as a virtual duo programmer, allowing developers to create top-notch code. Considered the best free AI code analyzer due to its training on large datasets from diverse sources while supporting several programming languages.

Key Features:

• It offers an understanding of the context.
• This tool assists in exploring new programming languages by producing code examples.
• It's capable of identifying any potential issues in your code.

Pros & Cons:

8. Amazon CodeWhisperer 

It provides prompt coding suggestions based on your style. Additionally, the platform is known for its API, which allows for smooth incorporation into programmers' current workflows.

Key Features:

• It combines effortlessly with other Amazon cloud offerings.
• Swiftly recognize and communicate coding mistakes.
• This tool is capable of producing comments and documentation for code.

Pros & Cons:

The Potential of AI-Powered Code Review 

The AI-driven code review world continues evolving. As large language models (LLMs) gain momentum and community-driven rule sets continue to grow, we're on the brink of exciting possibilities:

• Support for niche languages and frameworks have improved, and even older legacy systems can be covered with the help of AI
• Identify problems and suggest refactoring steps that are aware of the context. These steps should fit with your codebase's style and architecture.
• You can use AI-powered reviews in various platforms, CI/CD pipelines, and IDEs, making AI-assisted code reviews a standard part of your development workflow.
• As codebases contain sensitive information and are considered intellectual property, secure on-prem solutions will become more important for organizations who want to securely adopt AI

Developers and organizations can look forward to a new age of code assessment using strong AI technologies alongside the latest improvements in LLMs. This new era will be skilled and will adapt to their changing needs.

Closing the Gap

AI code review tools help solve the skills gap in software development. They allow organizations to deal with the shortage of experienced developers. By automating mostly repetitive and time-consuming tasks, these tools enable developers to focus on more valuable work, which improves their productivity.

Code review software can help junior developers improve their skills. AI code reviewers provide helpful guidance and learning opportunities, speeding up their mastery and shortening the onboarding process. AI tools also help development teams work better by producing clear, well-documented code. This code is easier to read and maintain, which lowers the chances of miscommunication and mistakes.

Natural language interfaces can also empower non-technical team members like product managers or business analysts to access technical knowledge easily. This makes product development lifecycles more efficient, and also reduces interruptions for engineering teams. 

Want to see how you can leverage AI to transform your development workflows? Book a demo today and experience the future of coding efficiency.