Artificial intelligence (AI) is entering various industries, offering numerous advantages to fields like software coding and development. Today, many development teams have adopted AI code review tools and noticed how much faster they catch errors and improve code quality. Yet, with any new technology, there are questions about its limitations, especially with security and accuracy.
AI coding tools are software solutions that leverage artificial intelligence to analyze and improve code. Traditionally, code reviews require peers or senior developers to conduct this process manually, but it can be time-consuming and prone to human oversight.
AI code review tools automate much of this process by using machine learning and natural language processing to detect anomalies, bugs, and security vulnerabilities in the code. These tools work by scanning code quickly to find common errors, security gaps, and inefficient logic. Then, it will provide suggestions for improvements.
Tools like GitHub Copilot, CodeRabbit, and Codium AI can analyze lines of code and flag problematic areas. They even offer fixes in real time or during the review process. The AI behind these tools requires data scientists to train them on massive datasets of code. This helps them learn from previous reviews and continuously improve suggestions over time.
AI code review tools had a market size of $4.86 billion in 2023, and researchers project it to grow at a compound annual growth rate of 27.1% from 2024 to 2030. This expansion suggests that more industries will use AI to streamline development and workflows and enhance code quality.
When developers use AI code review tools, many find they offer several of the following advantages:
Humans reviewing code vary in attention to detail and approach based on experience. Plus, the task itself takes time, which can cause burnout and make it more difficult to find flaws. Yet, with AI tools, developers can streamline the process as it maintains a uniform standard of analysis across all reviews.
They apply the same criteria to every piece of code, ensuring that vulnerabilities or errors do not exist. As such, they can reduce overlooked instances due to human error or fatigue. This consistency helps development teams maintain a higher standard of code quality, especially in large projects that require several team members.
The rate at which these tools operate is another major benefit. Since manual code reviews are time-consuming, AI solutions can analyze large amounts of code in a short period.
With their instantaneous speed, they can identify issues in a fraction of the time it would take a human. This quick analysis allows developers to receive real-time feedback, enabling them to optimize their code as they write it.
Developers are always on a time crunch, from dealing with unrealistic deadlines to fixing bugs and meeting new requirements. With limited availability to review code, developers can turn to AI tools whenever needed.
As a result of their 24/7 availability, they can complete their reviews right on time. This means professionals can maintain their momentum on projects and address issues as soon as they arise.
AI code review tools may have many advantages, but it is important for those working with code to consider their downfalls.
A pressing concern with AI code reviewers is they come with security risks. While these tools can identify vulnerabilities and improve code quality, they are not foolproof. In fact, Snyk's experience with AI code review tools revealed that these systems suggested insecure code. However, its survey found that 75.8% of respondents believed AI-generated code was more secure than human-written code.
Many developers feel confident about using these tools, but this may come from an increased need to understand how AI operates and its security concerns. While AI can be an asset, the same survey also revealed that 56% of developers admitted that these software solutions sometimes or often introduce coding issues like security vulnerabilities.
Therefore, it is important to educate technology teams about the limitations of secure development. Meanwhile, proper oversight is key, as it still needs human reviewers to guarantee reduced vulnerabilities.
AI tools have yet to reach perfection, as they can sometimes flag non-issues as potential problems. Or, they may fail to identify genuine code vulnerabilities. False positives can frustrate developers, as they force them to spend unnecessary time addressing warnings that are not a real threat to the code. Over time, this can lead to "alert fatigue," causing software professionals to dismiss valid warnings.
Conversely, false negatives are even more concerning because they allow actual code flaws to slip through the cracks. This issue may lead to inefficient or insecure code once it is live.
AI often lacks the ability to understand the broader context or intent behind the code. This can lead to inappropriate suggestions or missed issues that a human reviewer would likely catch.
For example, AI tools may flag certain parts of the code as inefficient or redundant without understanding why a developer made those choices to meet a certain requirement. Additionally, when reviewing specialized code, the tool may overlook dependencies that are critical to the application's proper functioning. This lack of contextual awareness can lead to issues for developers when they must ignore irrelevant feedback.
The benefits are clear with AI review tools:
However, it is crucial to use them with their limitations in mind. Given their drawbacks, AI tools are best for pre-reviews rather than full code reviews. In other words, they can be highly effective in catching surface-level issues early on, but a project should still require thorough human review.
When using AI for code review, developers can take advantage of these tools' full benefits. However, they do have limitations. That is why creators should use them thoughtfully. Leveraging their strengths while staying mindful of their weaknesses will allow for a more efficient and secure development process.
