AI-Powered Code Review and Guidelines

You’re a developer on a small front-end team working on a React-based food ordering app. The team just completed a sprint and merged their changes into the main branch. The QA team has already reported a functional bug (which you debugged in the previous lesson). However, now you’re tasked with a new responsibility: reviewing the code before final deployment.

You’re working on a React-based food ordering application where users can browse a menu, add items to their cart, and place an order. The app is live and functional, but good developers know that just because the code works doesn’t mean it’s written well.

During a peer code review of the recently updated Cart.js file, which handles the cart display and order functionality, you start noticing some issues in how the logic is structured and maintained. Everything seems to function as expected, but skilled developers know that working code is not always quality code.

While reviewing the recently updated Cart.js file, you notice some red flags:

• Inline logic that could be extracted for clarity.

• Lack of defensive checks and best practices.

• UI behavior that doesn’t match team conventions (e.g., unstyled disabled states).

• Missing documentation/comments for newly added logic.

You’re now responsible for conducting a code review, but instead of doing it all manually, you’ll tap into the power of GitHub Copilot for automated code quality improvements.

Before tackling code quality improvements, you need a local environment in which you can simulate the experience of a real pull request review. This step is foundational because reviewing code in isolation doesn’t reflect the real-world dynamics of team collaboration and iterative changes.

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