Explaining AI Code Review: Its Benefits, Components & More

AI code review is garnering traction to match the increase in demand for custom AI-based software solutions. It is an important step of SDLC and shouldn’t be compromised, as it defines code performance. While the traditional evaluation system is still one of the primary assessment models, AI integration fosters accessible, faster, and accurate systems. 

What is AI Code Review?

It is an automated code assessment process that uses multiple AI sub-technologies and dedicated tools to assist developers analyze code. The AI models are trained on a vast dataset containing coding data, best practices, common errors, etc., based on which the AI tool assesses code quality, consistency, and security measures.  

AI tools are integrated into the existing system that provides feedback on code, autofixes minor bugs that optimizes workflow, and frees up developers’ time. Large-scale businesses that want to automate code review with AI should understand the basics before investing, and that’s what this blog will cover. 

Today, we will discuss everything you need to know about AI code review, from advantages to process and tools to components. This will ensure you are digitally adept and aware when collaborating with an AI development company for your next project. 

Traditional Code Review vs AI Code Review

The traditional code review has been perfected over the years to reach its present-day speed and accuracy. An AI-powered reviewer, on the other hand, employs AI code review tools and other technologies to simplify code assessment. That being said, both methods are important in code evaluation. 

Here is a side-by-side comparison to better understand how these two approaches differ. 

The above comparison clearly shows that both offer different forms of code assessment experience. AI based code review does offer efficiency, but it still requires human supervision for better in-context and detailed feedback. Integrating AI for code review is like using a powerful tool to assist developers in accelerating the process; it cannot take over the whole process. 

However, AI-powered code review will definitely be an upgrade and significantly improve the development lifecycle. The tool would be especially useful in the case of AI-based software development as it simplifies the complexities of the modern development process.

Advantages of AI-based Code Review for Developers 

Process Acceleration 

Using AI for code review allows developers to identify minute errors, details that would otherwise require a lot of time and resources. It mitigates the constant back-and-forth between code testing and fixing by automating minor fixes and identifying complex issues.

Maintaining Up-To-Date Coding Standards 

The AI-powered code review tool gets regular updates and ensures up-to-date code compliance. Auto code compliance makes the assessment more thorough. It also flags inconsistencies or bad syntax quality for standardized code quality.   

Early Security Issues Detection 

AI-based code review fosters preventive measures by detecting the signs of security threats during the initial development phase. This allows the team to handle these bugs and issues before they become a major concern. 

Faster Employee Induction 

When trained on usual development policies and practices, an AI reviewer can be used to train new employees and help them become familiar with the company’s development practices. The instant feedback mechanism further accelerates the process while streamlining it for beginners.  

AI-Powered Code Review – A Complete Process

The AI tools assist with code review. And the whole process comprises two key stages: a) model training and fine-tuning to understand code structure, and b) the evaluation process, where actual codes are reviewed by AI-based code review tools. 

Model Training & Fine-Tuning  

Most of these AI tools are built on deep learning and transformer architecture. The model training introduces a massive set of code repositories and unsupervised learning techniques. This helps the tool understand code structures, syntax, and logic. 

Now, after training, the model is fine-tuned based on the requirement-specific data, such as: 

• Bug Detection 
• Code Summarization 
• Code Review Emulation 

5-Step AI Code Review Process

Once trained and fine-tuned, the AI tool is ready for code assessment. Here is a simplified 5-step process for AI-based code review: 

Step 1: Code Analysis

The AI code reviewer scans and breaks down the entire thing into smaller chunks for thorough analysis.

Step 2: Pattern Identification 

It recognizes code patterns and scans its existing dataset to compare the two, looking for potential issues, improvement scope, and best practices. 

Step 3: Potential Issue Detection & Flagging 

When comparing the input syntax with existing data, the AI code review tools flag existing issues, potential bottlenecks, and syntax errors.

Step 4: Suggestion & Explanation 

The AI tool not only flags but also offers a detailed explanation behind the suggestion. Some tools also auto-update minor errors and suggest possible solutions for complex ones. 

Step 5: Continuous Evolution 

The AI tool continually updates itself on the latest trends and coding practices to improve, maintain, and streamline future upgrades. 

05 Leading AI Code Review Tools

AI tools offer assistance throughout the SDLC. From integrated AI code review platforms to tools with real-time corrections capabilities, these AI tools significantly improve developers’ coding experience. 

We have selected five AI code review tools that would foster optimal code assessment: 

#1 GitHub Copilot

GitHub Copilot was developed as a collaborative effort of GitHub, OpenAI, and Microsoft. The tool generates summaries for pull requests, autocompletes code, and easily comprehends natural language commands. 

It supports multiple IDEs and programming languages, including VS Code, JetBrains, Python, JavaScript, TypeScript, and more. It spots every code error and provides contextually accurate suggestions.  

Related Read – Copilot vs ChatGPT: Best AI Assistant Compared  

#2 CodeRabbit 

CodeRabbit is a versatile AI code review platform that connects with the existing workflow and identifies code issues early to assist with future updates and maintenance. Another plus of this tool is its straightforward configuration, which requires minimal effort to set up. 

CodeRabbit works on various Git platforms and provides line-by-line feedback for comprehensive understanding. It also integrates with Jira and Linear, which enhances the development environment. 

#3 CodeAnt AI 

CodeAnt AI is for those who want to accelerate code assessment without compromising on the quality of the process. It easily handles complex code and auto-fixes minor errors and security flaws.  

This AI code review tool significantly reduces analysis time and offers a detailed summary of all the changes. Its versatility is evidenced by its compatibility with leading version control platforms and programming languages. 

#4 CodePeer 

CodePeer makes code review more accessible and organized for human testers. It speeds up the process through clarity, accountability, and streamlined collaboration. The intuitive interface and visible feedback allow everyone to stay on the same page. 

Its extensive functionalities include turn tracking and task progression to mitigate the constant back-and-forth in reviews and code assessment. It also offers a blocker and clear task management functionalities to have clarity throughout the review process. 

#5 Kodus Kody 

Kody is an open-source AI code review tool designed by Kodus. It is highly customizable, eventually trains itself to the business’s standards, and provides contextually accurate feedback. The tool is self-hosted on GitHub, which ensures data security. 

Kody allows a team of developers to customize coding guidelines and standards, and learns about the team’s architecture and coding patterns to provide relevant insights. 

AI-Powered Code Review & Human Touch 

There is no denying that AI-based code review will completely transform the assessment journey. These tools can automate the process, make it more thorough and precise, faster, etc., but can they replace a human? No.   

AI-powered tools are still an assistant that can enhance the process for human resources, not replace what they do. These tools require human supervision and intervention, especially in cases of complex problem resolution. 

That being said, in the era of digital advancements, where efficiency is the key defining factor of quality operations, these AI tools are also necessary. The tools optimize processes and help businesses run smoothly. 

So, it is advised for business owners to connect with a reliable service and integrate custom AI code review tools that understand the business’s vision and provide the required assistance.