Should Developers Use AI Code Review Tools?

AI code review tools like GitHub Copilot, CodeRabbit, and Codium AI are becoming increasingly popular, but they are not superior to human reviewers in every way.

Artificial intelligence (AI) is making its way into various industries, offering many advantages to areas such as software coding and development. Today, many development teams have adopted AI code review tools and found that they are able to find bugs faster and improve code quality. However, as with any emerging technology, AI is also accompanied by questions about its limitations, especially in terms of security and accuracy.

Understanding AI Code Review Tools

AI coding tools are software solutions that use artificial intelligence to analyze and improve code. Traditionally, code review requires peers or senior developers to manually conduct this process, but this can be time-consuming and susceptible to human oversight.

AI code review tools automate the code review process to a high degree by using machine learning and natural language processing to detect anomalies, errors, and security vulnerabilities in the code . These tools quickly scan the code to find common errors, security vulnerabilities , and inefficient logic. It will then provide suggestions for improvements.

Tools like GitHub Copilot, CodeRabbit, and Codium AI analyze lines of code and flag problem areas. They even offer fixes in real time or during a review. The AI ​​behind these tools requires data scientists to train them on large datasets of code. This helps them learn from previous reviews and improve their suggestions over time.

The market size for AI code review tools is expected to be $4.86 billion in 2023, and researchers expect it to grow at a compound annual growth rate of 27.1% from 2024 to 2030. This growth suggests that more industries will use AI to streamline development and workflows and improve code quality.

Advantages of using AI code review tools

When developers use AI code review tools, they find that these tools offer several benefits:

1. Consistency

Code reviewers vary in their attention to detail and experience-based approaches. In addition, the task itself requires a lot of time, which can lead to burnout, making it more difficult to find defects. However, using AI tools, developers can streamline the review process because such tools maintain a consistent and rigorous analysis standard across all review stages.

It applies the same standards to every piece of code, ensuring that there are no vulnerabilities or bugs. As a result, they reduce instances that are overlooked due to human error or fatigue. This consistency helps development teams maintain a higher standard of code quality, especially in large projects that require multiple team members to complete.

2. Speed

The speed at which these tools operate is another major benefit. Since manual code review is very time-consuming, AI solutions can analyze large amounts of code in a short period of time.

With their instantaneous speed, they can identify problems in a fraction of the time it would take a human. This rapid analysis allows developers to receive real-time feedback, enabling them to make optimizations as they write code.

3. Availability

From dealing with unrealistic deadlines to fixing bugs and meeting new requirements, developers are always under time pressure. With limited availability to review code, developers can turn to AI tools when needed.

With 24/7 availability, they can complete reviews on time. This means professionals can stay on track with projects and resolve issues as soon as they arise.

Disadvantages of using AI code review tools

AI code review tools may have many advantages, but it’s also important to consider their disadvantages for those who work with code.

1. Security Risks

A pressing concern for AI code reviewers is that these tools pose security risks. While they can identify vulnerabilities and improve code quality, they are not foolproof. In fact, security software developer Snyk's experience with AI code review tools shows that these tools may recommend unsafe code. However, the survey found that 75.8% of respondents believe that AI-generated code is safer than code written by humans.

Many developers feel confident in using these tools, a confidence that may stem from a pressing need to understand the operational aspects of AI and its security issues. While AI can be an asset, the same survey also showed that 56% of developers acknowledge that these software solutions sometimes or often introduce coding issues such as security vulnerabilities .

Therefore, it is very important for technical teams to understand the limitations of secure development. At the same time, proper supervision is key, as it still requires manual review to ensure that vulnerabilities are reduced .

2. False positives and false negatives

AI tools are not yet perfect, as they sometimes misidentify insignificant things as potential problems. Or, they may fail to identify true code vulnerabilities . False positives can frustrate developers because they have to spend a lot of time dealing with warnings that do not pose a real threat. Over time, this can lead to "alert fatigue," causing software professionals to ignore valid warnings.

In contrast, false negatives are more worrisome because they allow actual code defects to slip through. This problem can cause code to be inefficient or insecure at runtime.

3. Limited scene understanding capabilities

AI often lacks the ability to understand the broader context or intent behind the code. This can lead to inappropriate suggestions or miss issues that a human reviewer might have spotted.

For example, an AI tool might flag parts of the code as inefficient or redundant without understanding why the developer made those choices to meet specific needs. Additionally, when reviewing specialized code, the tool might overlook dependencies that are critical to the proper functioning of the application. Lack of contextual awareness can cause problems when developers must ignore irrelevant feedback.

Is it beneficial to use AI code review tools?

The benefits of using AI review tools are clear:

• Highly efficient in finding small problems.
• Save developers time identifying common errors and providing real-time feedback.
• Allows for more focus on complex or creative work.

However, it is crucial to consider their limitations when using them. Given their shortcomings, AI tools are best suited for pre-reviews rather than full code reviews. In other words, they are very effective at finding surface issues early on, but projects still require a thorough human review.

Striking the right balance with AI code review tools

When using AI for code review, developers can take advantage of the various strengths of these tools. However, they also have limitations. That’s why developers should use them wisely. By leveraging the strengths of these tools while avoiding their weaknesses, it will make their development and deployment processes more efficient and secure.

Original title: Should You Use AI Code Review Tools? , Author: Zac Amos