How to Automate Your Code Reviews in 2 Hours with AI

As a developer, nothing can be more tedious than manual code reviews. You know the drill: you write code, submit it for review, and then wait for feedback that often feels like it takes forever. But what if I told you that you could automate a significant chunk of this process using AI? In just two hours, you can set up tools that help streamline your code reviews, saving you time and making your workflow more efficient.

Prerequisites

Before diving in, make sure you have the following:

• A GitHub or GitLab account where your projects are hosted.
• Basic understanding of Git and version control.
• Access to a code editor and terminal.
• Familiarity with setting up integrations or webhooks.

Tools to Consider for Automating Code Reviews

Here’s a list of tools that can help you automate code reviews, each with its own strengths and weaknesses:

| Tool Name | Pricing | Best For | Limitations | Our Take | |------------------|----------------------------------|----------------------------------|-----------------------------------------------|-------------------------------------------| | SonarQube | Free, $150/mo for Pro | Static code analysis | Requires setup; can be complex for new users | We use it for quality checks on our code.| | CodeClimate | Free tier + $12/mo per user | Continuous integration | Limited free features; paid tier adds complexity | Great for teams; we prefer simpler tools.| | DeepSource | Free for open-source, $12/mo/user| Automated code reviews | Limited language support | We don't use it due to language restrictions.| | Codacy | Free tier + $15/mo per user | Quality assurance | Can be slow on larger codebases | We find it useful for quick checks. | | Reviewable | $15/mo per user | Review management | Not as feature-rich as others | We don’t use it; prefer more robust tools.| | Sourcery | Free tier + $12/mo per user | Code improvement suggestions | Limited support for some languages | We use it for Python projects. | | HoundCI | Free, $20/mo for advanced features| Integration with GitHub | Can be limited in flexibility | We don’t use it; setup can be cumbersome.| | GitHub Actions| Pay per usage | Custom workflows | Can be complex to set up for beginners | We use it for CI/CD but not specifically for reviews.| | Pull Panda | $49/mo for teams | Pull request management | Limited features in free tier | We don’t use it; prefer standalone tools.| | ReviewBot | $10/mo per user | Automated reviews | Less known; fewer integrations | We haven’t tried it yet. | | Lintly | Free, $30/mo for larger teams | Linting and style checks | Limited integrations with CI tools | We use it for JavaScript projects. | | MergeQueue | Free tier + custom pricing | Merge request automation | Can be slow with large PRs | Not in our stack; lacks features we need.| | Stylelint | Free | CSS and style linting | Limited to CSS only | We use it for frontend styling checks. |

What We Actually Use

In our experience, we heavily rely on SonarQube for static code analysis and Sourcery for Python projects. These tools help us catch issues early and improve code quality without the hassle of manual reviews.

Step-by-Step Setup

Step 1: Choose Your Tools

Select 1-2 tools from the list that best fit your workflow. For instance, if you primarily work with JavaScript, consider using Lintly alongside SonarQube.

Step 2: Set Up Your Environment

1. Install the Tool: Follow the installation instructions on the tool's website. Most tools offer a straightforward setup process.
2. Integrate with Your Repository: For tools like SonarQube, connect it to your GitHub or GitLab repository. This usually involves generating an API key and entering it into the tool's settings.

Step 3: Configure Your Rules

1. Define Code Quality Standards: Customize the rules based on your project requirements. Most tools allow you to select which rules to enforce.
2. Test the Integration: Create a dummy pull request to see if the tool catches issues as expected.

Step 4: Automate the Workflow

1. Set Up CI/CD: If using GitHub Actions, create a workflow file that triggers the code review tool on pull requests.
2. Run Automated Reviews: Ensure that every pull request is automatically reviewed by your selected tools. You can adjust the settings to fail builds based on code quality issues.

Step 5: Monitor and Iterate

1. Review Results Regularly: Check the reports generated by your tools to see if they align with your expectations.
2. Adjust Rules as Needed: Don’t hesitate to tweak the rules or add new ones based on your team's feedback.

Troubleshooting Common Issues

• The Tool Isn’t Running: Check your CI/CD configuration. Ensure that the correct permissions are set for the tool to access your repository.
• False Positives: Review the rules set in your tool. Sometimes, you may need to adjust them to fit your coding style better.
• Performance Issues: If your codebase is large, consider breaking it into smaller modules or optimizing the tool’s settings to improve performance.

What's Next?

Once your code review process is automated, consider exploring other areas of automation in your development workflow, like testing or deployment. Tools like GitHub Actions can help you create a fully automated CI/CD pipeline.

Conclusion

Automating your code reviews doesn’t have to be a daunting task. By selecting the right tools and following a straightforward setup process, you can save hours of manual work and improve your code quality significantly. Start today by picking a tool that fits your project needs and get it integrated into your workflow.