Code reviews are often the bane of the job for many developers, with many finding the process tedious and sometimes unrewarding.

Thankfully, AI-powered code review tools are helping revolutionize this process, identifying code smells, predicting bugs, and suggesting how the code can be improved—drastically reducing workloads in the process.

Thus, in this blog, we’ll explore the capability of modern AI-powered tools and their practical use cases in the code review process, ensuring software quality and accelerating development cycles.

Why Are Code Reviews Important?

Code reviews are a key stage in the software development lifecycle and involve developers meticulously assessing each other’s code to identify errors, inconsistencies, formatting issues, and the quality of the documentation provided.

Overall, the goal is to ensure no issues arise during the software integration process and that coding standards and project requirements are being adhered to.

Changing technologies and methodologies have seen code review processes evolve, although the core aim of the activity is to develop collective ownership over software in a collaborative environment.

Checking the code is correctly formatted and meets the required standards enhances the accuracy of software functions and boosts overall performance.

How Does AI Assist in Code Reviews?

AI turns code reviews into an automated process that quickly identifies issues and highlights how code can be improved.

Since they’re built on coding-specific, powerful algorithms, AI-powered code review tools can automatically identify and fix errors, optimize performance, and recommend possible improvements that developers can choose to implement.

AI significantly speeds up the process and can analyze huge amounts of code in seconds, checking each line against coding standards and the requirements set by the developer.

As a result, AI-aided tools spot obscure anomalies and patterns that would almost certainly be missed by a human. They do this in a way that is consistent and completely accurate, without allowing any bias to dictate decision-making or missing issues due to tiredness, which is possible in a human-led review.

Ultimately, AI-powered code review tools result in higher-quality, faster, and more accurate software. This is why, as well as being used by developers, more cybersecurity companies nowadays use this method to check if an organization’s source code is the reason for frequent attacks, regardless of the software and third-party protection being used.

Key Components of AI Code Reviews

On the surface, an AI code review consists of three things—input, output and reports. However, a fully-fledged AI code review actually contains five key components. Whether it’s a step in the process or a backend boost, these elements provide an advantage over traditional reviews, testing and Q&A:

1. Static Code Analysis

During static code analysis, the code is assessed without being executed to identify any possible issues, such as coding standard inconsistencies, syntax errors, and security flaws.

This stage is important, especially when analyzing large codebases, as AI can examine thousands of lines of code without missing errors. The tool can then use generative capabilities to produce a detailed report of all its findings and include recommendations for improving the software.

2. Rule-Based Checks

Once it conducts a static code scan, the tool then kickstarts a range of rule-based checks to analyze code to determine if it’s consistent with industry standards, best practices, formatting guidelines, and project requirements.

In addition, these checks and standardization provide a baseline for future code analysis, establishing a clear set of coding guidelines.

3. Dynamic Code Analysis

Afterwards, dynamic code analysis occurs—the AI observes how the software performs and identifies runtime errors or other issues that may impact functionality.

This vital step assesses how code interacts with connected systems and third-party dependencies to provide maximum insight into how the code behaves. Once completed, the AI tool will provide concise and targeted recommendations on how to mitigate the issue.

4. Natural Language Processing

NLP is a branch of CS and ML, revolving around how AI understands things contextually. As a result, code review tools can be trained on multi-petabyte datasets in order to establish enough points of reference.

Later, using this data, the model can recognize patterns and anomalies that can be used to identify potential issues, becoming more proficient each time new data is provided.

In the context of more complex business workflows, AI can extract data from an invoice and this can then be used alongside other data to train and develop ML models that check for output errors and miscalculations in code, ensuring complete accuracy in newly developed software.

More impressively, NLP allows models to learn from human input and comments to enable an even more adaptable review mechanism.

5. LLM APIs and integration

Contrary to popular belief, open-source coding LLMs are mostly reserved for personal use by those willing to invest time into tweaking and fine-tuning. Hence, AI code review tools would do best to use APIs from OpenAI, Google and Meta, taking advantage of their gargantuan resource pool and dev tools.

This takes code reviews to another level compared to standard ML models, focusing on nuances that could be missed using only basic training data.

Furthermore, LLMs can provide detailed explanations and comments in natural human language, highlighting their decision-making and recommendations in an easy-to-understand way.

Best Practices for Using AI in Code Reviews

While you definitely shouldn’t let any AI tool loose into your codebase, that doesn’t mean you can’t exploit their full potential. To make sure everything goes smoothly, you must:

Make Sure Models Are Trained and Updated Regularly

Any LLMs that power AI code review tools need to be updated and retrained regularly so that they adhere to the latest coding standards, working practices, and security protocols.

Not only does this ensure the code is correct, but it also identifies potential vulnerabilities by analyzing it against the latest security threats.

This is particularly important in large-scale code reviews, where errors can be easily missed if LLMs and static analysis tools are trained on outdated or irrelevant data.

Be Aware of the Importance of Humans-in-the-loop

AI should always be combined with human insights, combining the latest AI developments with logical human thought processes, resulting in a balanced and robust code review system.

After all, the human mind will always be able to provide more context than a machine, easily spotting false positives and making sensible judgment calls on recommendations offered by AI’s analysis.

Secure All Data

All data that is collected and processed before, during, and after the code review process needs to be protected to prevent it from falling into the wrong hands. This means using strong passwords, firewalls, and secure storage.

Important data shouldn’t be kept as a Sharepoint backup, for example, or another basic storage solution. Instead, valuable training data and insights should be stored using optimized cloud storage to avoid potential data breaches.

Establish Comprehensive Auditing and Reporting Processes

The effectiveness of code reviews depends on the quality and comprehensiveness of audits and reporting. No matter how good the tool is, if proper verification isn’t there, it’s all for naught.

Documenting each code review is vital to the success of future code reviews, providing ML models with relevant data to improve their decision-making. Furthermore, auditing is essential to maintain a high level of security, recording all vulnerabilities so they can be mitigated.

Conclusion

The transformative impact AI-powered code review tools have had and are having on the code review process is significant. These tools deliver high speeds and unrivaled accuracy, while offering insights into how software could be improved.

However, all AI tools are only as good as the data they are trained on, and because of this, they need to be provided with the latest information regarding coding standards and security vulnerabilities to be truly effective.