A code review for an average pull request takes 18 hours to get approved, based on Graphite’s analysis of 1.5 million pull requests. But what if you could speed up this process – and save up to 24 hours of work per sprint?
That’s the promise of AI-enhanced code reviews. Powered by machine learning (ML) and natural language processing (NLP), these review tools go beyond saving engineers time. They also minimize the risk of human error, especially in the menial, tedious parts of the code review.
Of course, human experts can’t completely step down from code reviews yet. Assessing whether a change would make the codebase incrementally better (as per Google’s guidelines) requires subjectivity and understanding of the context AI tools are incapable of.
That said, AI has the potential to make code reviews faster and mitigate the risk of overlooked errors and vulnerabilities. Here’s why.
The Role of Code Reviews in Quality Assurance
Code review is the systematic process of examining the code to:
Identify and fix logic errors
Remove security vulnerabilities
Ensure alignment with established coding practices
Improve code quality and consistency
Ensure code maintainability and readability
Code reviews are typically done following a pull request, i.e., when a new piece of code needs to be integrated into the program’s codebase. Developers can also regularly examine and analyze the source code to ensure the software product maintains its quality and remains secure.
How AI-Powered Code Review Tools Work
Let’s say you conduct an AI code review during a web app development project. Once you submit the front-end or back-end code to the tool, it will:
Gather initial data, such as the commit history and related files
Divide the code into logical units surrounded by the necessary context
Check imports and related functions to assess dependencies
Extract contextual information from the existing documentation and code comments
Clean up formatting inconsistencies and tag metadata to prepare the code for the review
Analyze the code for syntax errors, logic inconsistencies, code style violations, and potential bugs
Generate suggestions for improving code quality
Over time, AI code review tools can learn from previous code reviews and human feedback to improve output accuracy and adapt recommendations to developers’ preferences.
Tools using AI for code quality assurance typically rely on machine learning algorithms and natural language processing to perform both static and dynamic code analysis. While they may include some rule-based error detection, the true power of artificial intelligence lies in identifying complex patterns and dependencies and generating advanced fix suggestions.
Machine Learning Models
Machine learning (ML) models are trained on relevant datasets to discover specific patterns in the input. In code reviewing, this enables AI tools to detect complex anomalies and logic errors on a level comparable to humans.
ML is also the technology that enables tools to learn from historical data over time. For example, if developers consistently address a certain issue in a specific way, the tool can adapt its recommendations to reflect it.
Natural Language Processing (NLP)
Natural language processing, or NLP, is an AI technology that allows code review tools to “understand” the documentation (including style guides) and comments. For example, the AI tool can learn the standard naming conventions for functions and highlight any non-compliance with those conventions.
With its help, AI code reviews can take into account the wider context of the analyzed codebase. That leads to more accurate error identification and spot-on recommendations for improving code quality and performance.
Rule-Based Systems vs. AI Models
Rule-based systems are the precursor to AI-powered code review tools and continue to power some basic features like identifying syntax errors. They check whether the code follows predefined rules. For example, if a developer forgot to add the obligatory semicolon at the end of the line, a rule-based system will highlight it as a syntax mistake.
However, AI-powered tools are more advanced than their rule-based counterparts. Thanks to NLP and ML, they can learn from documentation and comments, adjust suggestions based on historical data, and predict security vulnerabilities more accurately.
Increase process efficiency. AI tools can automate the detection of common errors, allowing developers to focus on more value-adding parts of the process and speed up reviews. For example, Snyk reduced the mean time for vulnerability fixes by 62% for one Australian client.
Reduce human error. Sifting through hundreds or thousands of lines of code manually is a recipe for accidentally overlooking critical bugs and style violations. AI tools can mitigate this risk, as Snyk did for Telenor. The tool improved the company’s vulnerability risk posture by 49% and increased the number of critical vulnerabilities fixed tenfold.
Improve code consistency. An AI code review tool can enforce coding standards across multiple projects and teams, leading to better code readability and maintainability. For example, Codacy helped Bliss Applications do exactly that – and ensure consistent code quality as a result.
Easily scaling. AI tools can analyze large codebases or scale test coverage without losing code review performance, accuracy, or speed. Codacy, for example, helped increase test coverage from 23% to 64% for Stim.
Use Cases for AI Code Reviews
In practice, AI-driven code reviews have some persisting limitations. Some AI models are prone to hallucinations. The tool’s ability to understand context remains somewhat limited, too. For instance, AI code review tools may fail to notice duplicate code in pull requests – and return invalid suggestions in rare instances.
Developers shouldn’t blindly rely on the AI tool’s output. Instead, developers should serve as the ultimate arbitrators on what changes are made to the codebase.
That said, current AI code review tools remain well-suited for the following three use cases.
Bug Detection
AI tools perform both static and dynamic code review to identify bugs. Those bugs can include memory leaks, null pointer exceptions, and logic errors. For example, Snyk’s code checker can catch invalid time/date formatting, expression logic errors, process/threading deadlock problems, and null dereferences.
Security Vulnerabilities
Certain tools like Snyk are geared toward security vulnerability assessments, while others (e.g., Bito) combine security risk identification with other code review capabilities. The common security risks that AI code review tools catch include:
SQL and code injection risks
Insecure API integration
Missing input data sanitization
Protocol insecurities
Weak cryptography algorithms
Man-in-the-middle attack risks
Unsafe password handling
Code Refactoring Suggestions
Based on the identified issues, AI code review tools can provide actionable suggestions for addressing them. For example, Bito’s AI code reviewer can provide tailored recommendations to improve front-end code performance and readability, along with addressing UI issues. CodeRabbit, in turn, offers one-click fixes for the revealed issues.
What Is the Future of AI in Code Reviews?
Based on the most recent Stack Overflow Developer Survey, 56.7% of developers are already using AI for debugging. However, the adoption rates are lower for testing code (27.2%) and committing and reviewing code (13.2%).
That said, 40.9% of the surveyed developers are interested in using AI for code reviews in the future. On top of that, 73% of respondents expect AI to be more or much more integrated into this part of the development process in the following year.
So, it’s safe to say that AI code review tools are poised to become the new normal in the near future. Soon enough, we may see AI-driven code reviews integrated into the DevOps methodology and CI/CD pipelines as a means of automation.
AI-enhanced code reviews may not be the industry standard yet, but they offer undeniable advantages to developers. After all, AI can reduce reliance on manual processes, improve bug detection accuracy, and speed up fixes.
However, using AI for code quality assurance requires understanding the limitations of those tools. They can produce false positives and negatives and can’t consider the code’s context the same way experienced developers do.
So, if you’re planning to introduce an AI code review tool in your software development lifecycle, make sure you do it responsibly. That, of course, requires AI expertise – the expertise that Integrio possesses and is ready to share under a variety of engagement models, including IT consulting and staff augmentation.
Need AI expertise to set up AI-enhanced code-reviewing processes? Discover how Integrio’s AI staff augmentation services can help you quickly close this talent gap.
FAQ
Most AI code review tools can be integrated with popular local IDEs like Visual Studio Code and JetBrains via extension install. You can also add most AI code review tools directly to your Git workflow thanks to integrations with GitHub, GitLab, or Bitbucket.
AI models may produce false negatives and positives and struggle with understanding the context for code changes and coding decisions. AI tools may also fail to notice duplicate code in pull requests.
Using a third-party tool to analyze your proprietary codebase may pose security and privacy risks. Other challenges include identifying invalid suggestions and tuning the model to your specific needs.
AI is highly unlikely to completely replace developers as it can’t show comparable problem-solving and creative thinking skills. AI tools also can’t take into account the code review context that was undocumented.
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