4 Advantages of using AI code review

AI code review enhances speed and accuracy, improving developer and security productivity.

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Reviewing code is critical to building high-quality, secure applications that can scale. However, companies have limited human resources and only so many hours to dedicate to manual code reviews. By augmenting human efforts with AI code reviews, organizations can spot bugs, address publicly disclosed vulnerabilities, and identify potential security risks.

What is an AI code review?

AI code reviews employs artificial intelligence (AI) algorithms to evaluate software code for errors, bugs, potential security vulnerabilities, performance issues, or best practices violations. It analyzes the code for patterns and structures that may indicate these issues, allowing developers to address them earlier in the software development cycle.

4 advantages of AI for automating code reviews

An AI code review is a powerful tool for optimizing software development. The advantages of automating code review with AI include:

  1. Improving overall quality

  2. Reducing opportunities for human error

  3. Boosting dev productivity without slowing them down

  4. Finding known and unknown issues

And then all of these advantages are then further augmented by the fact that AI learns with you, creating a feedback loop of improvement.

1. Improving overall quality

AI code reviews detect issues like security vulnerabilities, code smells, performance bottlenecks, and more. These reviews identify patterns and recommend code improvements that increase efficiency and maintainability while reducing technical debt.

Many of the benefits of AI code reviews, like an increased ability to detect issues human reviewers miss, stem from the speed and capacity of AI. An AI model has the ability to consume vast amounts of data in a fraction of the time, as well as keep up with latest coding trends, changes, vulnerabilities, and fixes far better than humans ever could. A non-AI SAST can't offer this. For example, because AI can cover more ground more quickly than humans when scanning code patterns across a multitude of languages, it can identify novel vulnerabilities across different languages swiftly and early. This results in a more proactive approach to security, helping organizations to identify and remediate potential security risks before malicious actors can exploit them.

AI code review also supports development teams to achieve higher code quality standards while reducing the cognitive load associated with manual code review. Leveraging the power of AI allows devs to focus on more strategic and creative aspects of development. 

2. Reducing opportunities for human error

AI, like humans, isn’t foolproof. However, feeding AI code review algorithms with large sets of code samples and leveraging ML techniques, results in AI algorithms growing more precise over time — and they generally won't make the same mistake twice. Eventually, this should make code reviews even more accurate and reliable, and developers will begin to leave more and more of the review process in the digital hands of the algorithm (though, as we always say, machines are fallible and human overview should generally be part of the process). 

AI code review algorithms can also be designed in specific programming languages and best practices. Doing so helps the AI to locate issues specific to a particular language or coding convention, improving the overall code quality of the software.

3. Boosting developer productivity 

Leveraging AI for code review frees developers to focus on other parts of their job. AI algorithms can review thousands of lines of code per second, compared to a human reviewer, who will likely top out at a few hundred lines per hour. 

Plus, AI never gets tired and can operate 24/7, facilitating continuous code monitoring in large codebases or projects with frequent updates. With AI code review, devs can redirect their energy and time to other priorities.

4. Finding known & unknown issue types with SAST & DAST

Static application security testing (SAST) and dynamic application security testing (DAST) are two techniques used in application security. Both methods help identify vulnerabilities in the application but differ in how they operate.

SAST is white-box testing and focuses on source code. The tool locates potential security flaws, such as buffer overflows, SQL injection, and cross-site scripting (XSS). SAST tools analyze the code for known patterns of security vulnerabilities and can identify issues like insecure coding practices, hardcoded credentials, and more. AI can enhance SAST tools with additional capabilities such as learning from rules and suggesting fixes for vulnerabilities — check out how Snyk uses the Deepcode AI engine for Snyk Code SAST to learn more. 

DAST, on the other hand, is black-box testing, which tests the application by sending requests and analyzing responses from the application. DAST tools can detect vulnerabilities that are not visible in the source code, such as input validation issues, session management flaws, and more. 

Using SAST and DAST scans together means security teams can find known and unknown vulns and have an optimal AppSec security posture as their systems scale.

On-demand SAST demo

Watch our recorded demo to see how teams can find and fix vulnerabilities with Snyk Code for SAST.

Terminology in AI can get confusing, so make sure to check out our AI Glossary to get a better utilization of AI tools.

The human-in-the-loop in AI code review

No automated code checker is perfect. We are the only ones in the industry doing human-in-the-Loop AI code reviews. As the name suggests, this hybrid approach means AI performs the bulk of the review, and a human steps in to review, annotate and decide if the achieved rule quality will be published. 

False positives and false negatives 

False positives occur when the AI algorithm says a code segment has an error or anomaly when there is nothing wrong with it. Typically, this leads to wasted time and resources in investigating a problem that isn't there. 

False negatives occur when the AI algorithm fails to identify problems in the code. This failure to detect flaws opens the software up to performance issues and security vulnerabilities that may be costly (in terms of both time and money).

AI code reviews hold the potential for false positives and negatives (don’t be fooled by the security tool that produces a long list of flagged issues — volume doesn’t necessarily equate with quality), which means a human should review the output and minimize the potential for these errors. The human-in-the-loop approach combines AI's speed and efficiency with humans' judgment and expertise, creating a more accurate and effective code review process.

Secure your code with Snyk for a developer-friendly experience 

Many traditional SAST tools on the market are limited by lengthy scan times and poor accuracy, resulting in many false positives and eroding developer trust in the technology. Snyk Code is different. It’s an efficient and actionable SAST that provides real-time security analyses with full application context in seconds and AI semantic analysis to identify and fix genuine, relevant vulnerabilities faster and proactively.

Snyk Code uses AI to manage risks for security teams and augment developer experience, helping to create more efficient teams and more secure products.


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