AI code review

AI code review is the use of artificial intelligence (AI) tools and techniques to assist in reviewing code for quality, style and functionality.

The automated process uses machine learning (ML) models to identify inconsistencies with coding standards and detect security issues and vulnerabilities.

AI code review tools often provide suggestions or even automated fixes, helping developers save time and improve code quality. They can be integrated into development environments and version control systems to facilitate continuous integration and continuous delivery (CI/CD) practices. Examples of these tools include GitHub Copilot, DeepCode, SonarQube and What the Diff.

The ever-changing landscape of software development demands a high-quality codebase. Therefore, teams are increasingly turning to open source repositories to accelerate projects and needing to manage code changes effectively.

AI plays a transformative role in code review as the approach is transforming the way developers maintain code quality and ultimately support the thriving ecosystem of software development. AI code review is an innovative approach that can use generative AI to enhance the traditional code review process.

With the ability to learn from vast amounts of open source code, AI systems can recognize patterns, flag potential bugs, and suggest improvements, fostering a culture of collaboration and continuous improvement.

There are four key components to AI code review that all play a crucial role.¹

• Static code analysis

• Dynamic code analysis

• Rule-based systems

• Natural Language Processing (NLP) and large language models (LLMs)

This is a method that analyzes source code before running a program. The purpose and main benefit is identifying issues or errors before running. Static code analysis can find bugs early, identify security issues, and improve maintainability, making it a crucial component to the code review process.

Static code analysis tools can analyze source code at the programming language level, making it especially useful for more complex codebases. With the proper tools in place static code analysis can scan through thousands of lines of code in seconds, saving companies precious time and resources. Once the static code analysis is performed AI algorithms can then take this information to recommend improvements or new course of action.

Unlike static code analysis, dynamic code analysis is designed to test the code or run the application for potential issues or security vulnerabilities. The benefit of this method is that it can test for problems while the software is running and find issues that might not be caught when the code is static.

Dynamic code analysis is also known as Dynamic Application Security Testing (DSAT). These DSAT tools have a dictionary of known vulnerabilities to look for when an application is running. These tools then analyze the responses to the inputs and make records of any issues. The dynamic code analysis tools can bring peace of mind to developers by finding performance bottlenecks and security vulnerabilities well before an application is live for customers.

This computer system uses predefined rules and best practices for code analysis. The benefit to this method is that it is applying logic to input data to reach a conclusion and plays a key role in the code review process overall. The rules help ensure that the code is meeting the industry standards and adhering to company guidelines.

This rule-based system establishes a consistent baseline for code analysis and can provide development teams with a reliable source of code analysis. A tool like linters examines code for syntax errors or deviation from a particular coding style and can correct the application, helping ensure good code quality.

NLP models, trained on large datasets of code, make up the heart of AI code review. These models are crucial to AI code review as they learn to recognize patterns in the code that might signal issues or inefficiencies. The goal is that, if used over time, the NLPs will start to get better at catching errors and making more detailed recommendations.

Separately, LLMs like GPT-4, are starting to be incorporated into code review tools. LLMs are able to understand the structure and logic of code on a more complex level than traditional machine learning techniques. The LLM method can identify more nuanced anomalies and errors, which contribute to more thorough code review.

watsonx Code Assistant™: The watsonx Code Assistant solution uses generative AI to accelerate development while maintaining the principles of trust and security. With watsonx Code Assistant developers can reduce errors, minimize the learning curve, and build quality code through code generation, code matching and code modernization.

Codacy: Codacy offers automated code reviews that support languages like JavaScript and Python, helping developers maintain code quality across their projects. The onboarding process is designed to integrate seamlessly into software workflows, enabling teams to catch issues early.

DeepCode: DeepCode uses AI to analyze code in real-time, providing actionable insights for open source projects. This tool enhances the onboarding experience for new developers by identifying common pitfalls and promoting best practices in software engineering.

Bito AI: Bito AI focuses on streamlining onboarding for software engineering teams with its intuitive interface and AI-powered code reviews. It can provide immediate feedback and actionable recommendations and help new team members adapt quickly to the company’s coding standards and best practices.

PullRequest: PullRequest offers both AI-driven insights and human expertise, facilitating a smooth onboarding process for software engineering teams. The platform encourages collaboration and knowledge sharing to encourage newer developers to learn from experiences reviewers.

Coderabbit: Coderabbit is an AI code review platform that uses AI tools to produce analysis and clear feedback. It delivers human-like reviews and is customizable as it works with all programming languages.

AI-powered code review can offer many different benefits for an organization and its development team, including:

• Efficiency
• Consistency
• Error detection
• Enhanced learning

One of the primary benefits of AI code review is efficiency. A traditional code review process can take quite some time and can also take many resources. With automated code review, like AI, the process can be done in moments. Each method of AI code review discussed earlier is a crucial part of an application development process from start to finish.

An example of this benefit is the IBM watsonx Code Assistant for Z. This gen AI-assisted product was built to accelerate the mainframe application lifecycle and streamline modernization, making it more efficient and more cost effective. Developers can automatically refactor selected elements, optimize code and modernize with COBOL to Java transformation.²

Human code review team members can be impacted by outside influences, such as fatigue or biases, which can lead to inconsistent reviews. AI is able to analyze code accurately and consistently, no matter the quantity or complexity, making it a key benefit of AI code review. Code review is a time-consuming process that can benefit from advanced technology like generative AI tools if used properly.

An example of consistency is IBM’s Granite model, which was trained on a large code base consisting of 115 programming languages and 1.63 trillion tokens. The AI code review method is straightforward and trained on various datasets. In addition, the Granite models used in training undergo a designed governance, risk and compliance (GRC) review process.

Because AI-powered code review tools are based on technology, the tools are highly effective at detecting in-depth errors in real-time oftentimes overlooked through manual review, such as code smells. Sometimes they are missed because they are subtle or only occurring under certain conditions unless run through certain code review methods.

One example is IBM Research has enhanced its IBM AIOps Insights platform to increase the speed at which IT experts find a solution to an IT issue. Through the power of LLMs and gen AI, AIOps Insights can gather data from a clients IT environment and find correlations in the data to identify potential issues.

A benefit of AI code review is that it can be a valuable learning opportunity for developers seeking to improve their coding skills long term. A huge benefit to AI is that it provides developers lots of feedback and recommendations that can ultimately change development workflows and help ensure that developers are learning to produce quality code.

Building off of the previous example, IBM AIOps Insights can bring together the human insights and AI-powered coding. With the help of an intelligent remediation module, a developer can take the necessary steps to trace the causes of a slowdown or technical issue with the system. This eliminates the need for a developer to write their own script to carry out remediation.

Developers might become overly dependent on AI tools for streamlining code review processes, leading to a diminished emphasis on personal expertise and critical thinking. This reliance can result in unchecked technical debt, as developers overlook deeper issues that require human oversight.

A way to overcome these challenges is to put ethical standards in place for the code review process and make sure that all developers abide by these rules. An organization should set boundaries to prevent any misuse and try to strike a balance between ethics and speed. Most importantly, the human element is still the most important factor in code review and AI is only augmenting the processes.

AI tools often struggle with specific context of a project, including the intricacies of APIs and overall architecture. This lack of contextual understanding can lead to inadequate validation of code quality and missed opportunities for optimizations that align with project goals.

When there are large amounts of data, it’s important to use training datasets that are diverse and represent all groups the organization is trying to target. Another way to overcome these limitations is to regularly check the AI system for biases through automated monitoring and setting up strict guidelines for gen AI to follow.

AI code review systems can generate false positives, incorrectly flagging code as problematic or false negatives, missing actual flaws. These inaccuracies can complicate the code review process, leading to wasted time on unnecessary fixes or unaddressed issues that contributed to increased technical debt.

A possible solution to this challenge is to use ML algorithms to monitor large amounts of data and take the time to learn how each metric behaves. When there is a baseline to refer to it can cut down on any false results and ultimately help developers adjust severity levels for false negatives. Separately, a way to overcome this AI code review challenge is to retrain the model on the same dataset, but alter the output values to better align with previous results.

Getting started with AI code review can significantly enhance a software development process by helping teams maintain high code quality and efficiency. There are a few general steps to effectively integrate AI-driven tools into a businesses code review workflow.³

1. Choose the right AI code review tool: To start, select an AI code review tool that fits the organization’s needs. Many of the popular options offer various features, including support for multiple programming languages and integration with existing workflows. Organizations should look for tools that provide metrics to assess code quality, such as code complexity, duplication rates and adherence to coding standards. These metrics help an organization set benchmarks for its development process.
    

2. Set up onboarding and configuration: Once a tool has been chosen the next step is onboarding the team. This requires clear documentation and training sessions to familiarize everyone with the tool’s features and capabilities. Organizations need to configure tools to align with coding standards and specific project requirements, which might include setting up custom rules or thresholds for specific metrics.
    

3. Incorporate AI in the review process: The next step is integrating the AI tool into the organization’s existing code review process. The AI generates review comments based on its analysis, highlighting potential issues and suggesting improvements. This process will not only streamline the review process, but will also allow developers to learn from feedback over time.
    

4. Use metrics to drive improvements: Organizations should take the information from the AI code review and use those metrics to track a team’s performance. By monitoring trends in code quality over time, development teams can point to areas of improvement. Furthermore, teams can use these insights during team meetings and generate ideas for how to address recurring issues and improve coding practices.
    

5. Balance AI and human insights: AI-driven code review tools can vastly improve the code review process, but it’s essential to balance automated feedback with human insights. Organizations should encourage team members to review AI-generated feedback and provide their own perspectives. This collaborative approach can bolster the review process and also foster a culture of learning and continuous improvement from team members.