Code review is a critical step in the application development process that verifies that new code meets the set quality standard. However, when done manually, this step introduces delays and consumes too much of developers' time and effort. This presents a challenge: how can code reviews scale efficiently while maintaining quality standards?  

What if the initial phase of code review could be partially automated? This is the premise behind AI-driven code review tools now available to DevOps teams.

In this article, we will explore how AI-driven code review benefits developers, DevOps teams, and businesses, as well as its impacts on DevOps culture.

Key Takeaways 

• AI-driven code reviews help accelerate software development cycles without sacrificing quality. 
• Automation saves developer time otherwise spent fixing defects late in the process. 
• Faster fixes and feature delivery improve customer satisfaction, while early issue detection cuts debugging costs. 

What Does an AI-Driven Code Review Look Like?

Advanced AI systems designed for code review duties are first trained on a company’s preferred coding standards, architectural patterns, and quality best practices. They learn what constitutes ideal code structure and style. When a pull request is submitted for review, the AI system will automatically analyze the proposed changes, flagging any lines of code or patterns that appear to deviate from norms or that its training has indicated could represent potential issues down the line. 

For example, an AI reviewer may highlight a section of code with many nested if/else statements, knowing that readability and maintainability suffer in complex conditional logic. It would present the flagged code alongside an explanation, such as “code complexity concerns” or “consider refactoring for clarity.” A developer would then inspect the flagged code more carefully and reorganize the logic to make it less complex.

Some potential issues that may be flagged by an AI review system are:

• Syntax errors, typos, and formatting issues
• Code smells, anti-patterns, and bad practices
• Security vulnerabilities and risks
• Performance bottlenecks and inefficiencies
• Compatibility and dependency issues
• Code duplication and complexity
• Test coverage and quality
• Documentation and comments

To get a better sense of what AI-driven code review looks like, check out our article: GitHub Copilot: Revolutionizing Code Generation with AI-Powered Assistance

If you want to get ahead in AI, consider enrolling in our Mastering Generative AI with OpenAI course.

Benefits for Developers

Below are some of the benefits of AI-driven code reviews:

• Detecting more issues than human review alone: AI-driven code review helps programmers find and fix more issues in their code than human review, especially security, performance, and complexity issues.
• Saves time by fixing bugs early: AI-driven code review helps developers analyze their code as they write it instead of waiting until later in the cycle. This helps them avoid spending hours debugging and fixing bugs later on.
• Helping in building an easily maintainable code base: By enforcing the organization’s code standards and best practices, AI-driven code review can help developers build an easily maintainable code base that is consistent, readable, and well-documented.

Learn more about how AWS’ AI-driven code review tool, CodeWhisper, from this blog: AWS CodeWhisperer: A Beginner's Guide to Cloud Development with AWS Tools

Benefits for DevOps Teams  

In addition to supporting developers, AI-driven code review also offers these benefits for DevOps teams:

• Scaling code reviews to large codebases and frequent pull requests (PRs) without slowing down cycles: When the codebase and the PRs get bigger, it takes longer and longer for DevOps teams to review the code. AI-driven code review can help scale the code review process by automating and accelerating the analysis and feedback of the code without compromising the quality and accuracy of the code review. This can help DevOps teams keep up with the pace of the DevOps cycle, which demands fast and consistent delivery of high-quality code.
• Optimizing code standards over time by providing metrics on top issues flagged by AI: By providing metrics and insights on the top issues flagged by AI, AI-driven code review can help DevOps teams optimize their code standards and best practices over time. This can help DevOps teams improve their code quality and maintainability and reduce the technical debt of their code.

Benefits for Business

AI-driven code review also offers the following business benefits:

• Deliver faster and better features to your users: AI-driven code review helps DevOps teams release new features and fixes more quickly. This gives a business a competitive advantage and helps meet the expectations of its customers.
• Early issue detection helps improve app quality and reliability: By detecting and resolving issues early in the code review process, AI-driven code review can help reduce the risk of bugs, errors, and failures in the production environment. This helps a business enhance its brand image and reputation to customers.
• Save money and resources in the long run: AI-driven code review can help you save time and effort, lowering application development costs. In the long run, it makes a DevOps team more efficient and productive, reducing the losses caused by poor code quality and reliability.

Impact on DevOps Culture  

AI-driven review tools are helping to streamline the code review process, freeing team members from tedious manual code checks so they can focus on the bigger picture - designing solutions that meet users' needs. 

Rather than rushing through reviews, teams now have opportunities for richer discussion as AI summarizes the code's workings and highlights areas for discussion. Developers gain insights from each other as they share their views on the highlighted areas. This nurtures a collaborative culture that can lead to more innovation, stronger teams, and increased productivity.

With the progress of AI-driven review, teams will get more impactful assistance and guidance. This will speed up development cycles without sacrificing quality. Additionally, developers will be more motivated as tedious tasks are automated, and their effort is directed toward building new features. 

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Continued Advancement  

Because AI-driven review tools are trained on growing data sets, their ability to recognize coding issues and recommend high-quality solutions will continue to advance. Through continuous learning, these systems will offer more insightful feedback in the future, which will help developers increase code quality and delivery speed. 

Companies pioneering this technology envision a future where AI proposes precise, proven fixes for common problems. With the right investments and ongoing development, AI has the potential to play an even bigger role in the application development lifecycle. 

Conclusion 

Code review powered by AI is a trend that is changing the DevOps cycle by providing intelligent feedback, suggestions, and insights to improve code quality. This results in many benefits to developers, DevOps teams, and businesses. As AI systems performing reviews become more advanced and capable, they will soon play an even bigger role in the application development lifecycle.

Please let us know your thoughts in the comment section below. 

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