There are many benefits to be gained from incorporating code review into your software development process. Most obvious are the quality improvements you’ll see in your product in the form of fewer errors and security vulnerabilities.
But there are many other benefits your team can experience from the code review process. For newer in career developers or developers who are new to your company, code reviews offer a way for them to learn about coding standards and practices used at your company and sets up a structured way for teams to share knowledge. Code reviews ensure better consistency and encourage better documentation because developers must communicate with one another through the tooling and code for the code review process to run smoothly.
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Manual code review
Manual code review, also called peer review, is where another developer reviews your code before you integrate your changes into the main branch. The reviewer is often a person on your team – sometimes with more experience so they can simultaneously act as a mentor – and other times there may be several people involved in a manual code review. The reviewer is looking for syntax and logic errors while also ensuring that the code meets company coding standards. Sometimes people from other teams will be included in the code reviews to spot any potential issues in features that interact with code being developed on another team. Managers may also be involved in reviews to verify the agreed-upon design has been executed the way they expected and to help guide everyone on the team toward the unified vision for their product.
Many version control systems have options to configure required code reviews as part of the check-in or integration process. When a developer wants to check in their code the tools will assign a code reviewer and require their signoff (in the form of a field update in the tool) before the new code can be successfully checked in or integrated into the branch.
Automated code review
Automated code review involves using static and dynamic analysis tools to scan your code for well-defined problems. Static analysis tools analyze the written code, looking for problems with syntax, logic, coding style, standards violations, and security vulnerabilities. Dynamic analysis tools attempt to run the code and look for bugs, security vulnerabilities, and performance issues.
Automated code review features may be built into your IDE (integrated development environment) or version control system. They can be configured to run on a regular schedule, such as nightly, across the entire codebase – identifying issues with code that’s already integrated, code that’s ready to be checked in, and code that’s still in progress. Some systems will open bugs and assign them to the code owner automatically or generate reports detailing the issues found in each automated code review run. Catching these errors early reduces the cost of finding and fixing them later and ensures your code contains fewer errors before it goes through manual code review, allowing your peers to focus on higher-level review concepts like design and how your code contributes to the overall product.
AI code review
Manual code reviews are prone to human error and automated code reviews are not as good at picking out complex errors in sophisticated code. AI code reviews use machine learning models to review code, make suggestions, and even fix errors on behalf of the developer. AI leverages large data sets to learn coding patterns and best practices to analyze code and identify errors and style deviations. Developers can also use large language models (LLMs) to perform in-depth code analysis and generate more robust code comments, making the code easier to maintain and freeing up time for the developer to focus on writing more code. Humans can review the AI output and correct any errors or adjust style preferences, helping the AI to learn and improve its ability to identify issues consistently.
Manual, automated or AI code review?
The best way to get the most out of code reviews is to employ all of the methods discussed in this article. Each has strengths and weaknesses and by using a combination of manual, automated, and AI code review – all with human oversight and final reviews – you can increase code quality efficiently across your organization.
By using a combination of manual, automated, and AI-assisted code reviews, organizations can create a well-rounded and effective review process. Each method helps compensate for the weaknesses of the others and ensures a more comprehensive code review. Manual reviews handle complex design issues and project-specific details that automated tools might miss. Automated reviews quickly catch syntax errors and enforce coding standards, providing fast and consistent feedback. AI code review brings advanced pattern recognition and spots subtle bugs and performance issues that may escape human reviewers. This balanced approach ensures that all aspects of code quality are addressed, leading to more robust, maintainable, and high-quality code.
Code review with Assembla
Assembla supports manual code reviews with built-in lightweight code review tools in every version control repo, whether you are using Git, SVN, or Perforce. Merge requests offer a common way to kick off and manage the code review process. Once the merge request is created you can add followers and @Mention teammates to request peer reviews. Inline threaded comments make it easy to have conversations about specific sections of code and keep track of what changes are being requested. Multiple tickets and commits can be associated with a merge request, allowing developers to keep related features together during review and merge them into the destination branch at the same time once reviews are complete. For a walkthrough of how to best leverage code reviews in Assembla see Merge Requests: Code Review in Assembla.
Using Assembla’s all-in-one source code and project management platform offers several key benefits when performing code reviews. Assembla enhances visibility by providing real-time insights into merge requests, code reviews and project status. This integration increases efficiency by streamlining workflows and reducing the need to switch between different tools. It also ensures better accountability, with clear task assignments and tracking. Ultimately, this unified approach leads to improved code quality by consistently enforcing standards and best practices. By making Assembla the final step in your review process, you ensure that manual code reviews serve as the last layer of oversight, catching any nuanced issues that automated and AI tools might miss.