Reducing PR Review Backlog with Cursor: Context Sharing Approach

This post was written and edited with AI, then published after operator review. Thumbnails may also be AI-generated.

It’s Friday afternoon, and a Pull Request (PR) with 50 file changes just landed. It includes a large-scale refactoring essential for a new feature, resulting in extensive modifications. Colleagues leave comments like ‘I’ll look at it Monday,’ and soon, next week’s deployment schedule starts slipping indefinitely. Reviewers feel overwhelmed, unsure where to start, while the PR author wastes time resolving conflicts caused by delayed reviews.

The root cause of this situation is a lack of context. Reviewers must expend significant cognitive resources to grasp the entire context of the changed code. Tracing all function call relationships and evaluating potential side effects is tedious and challenging. Consequently, reviews often focus only on superficial code style, or a hasty ‘LGTM’ might be given without properly examining the core logic.

In this post, I’ll compare how AI-powered code editor Cursor can solve this problem, using a Before/After format. I’ll cover specific methods that leverage AI’s ability to understand and summarize the entire codebase, reducing reviewer burden and enabling more in-depth reviews.

© AI Generated Image

Before: Manual Reviews, Scrambling for Context

Traditional code review methods typically follow these steps:

1. Branch Checkout: Pull the PR branch to your local environment. If it’s not up-to-date, an additional step of merging with the base branch is required.
2. Identify Changes: Open the list of changed files one by one in the IDE. With many files, it’s hard to prioritize which ones to examine first.
3. Analyze Impact: Manually trace all call sites to understand where a specific function’s change might have an impact. While relying on the IDE’s ‘Find Usages’ feature, dynamically called parts or implementations hidden behind interfaces are often missed.
4. Questions and Answers: Leave comments on the PR for unclear parts. The review pauses while waiting for the author’s response. Even after a reply, it takes time to recall the original context.

The biggest problem with this process is that the entire burden relies on the reviewer’s individual experience and memory. Especially with complex legacy systems or codebases intertwined with multiple domains, it’s almost impossible for one developer to grasp all the context.

After: Context-Based Reviews with AI

Cursor offers AI chat functionality that understands your entire codebase. This dramatically changes the review workflow. Let’s explore how both PR authors and reviewers can utilize it.

PR Author’s Preparation Process

Before requesting a review, the author can use Cursor to preemptively summarize information reviewers might find useful. This is a crucial step to improve review quality and save time.

Use the @Codebase symbol to ask questions about all files in the project.

@Codebase Summarize the main changes in this PR from a business perspective.
Also, identify about three potential areas where side effects might occur.

To answer this question, the AI analyzes the relationships between changed files and summarizes the core logic alterations. Adding this generated summary to the PR description allows reviewers to grasp the overall picture before diving into the code.

Reviewer’s Exploration Process

Reviewers no longer need to read every line of code. Instead, they can quickly dive into the core by asking the AI questions.

After pulling the branch locally and opening it with Cursor, select the function or class you’re curious about and ask in the AI chat window:

Which API endpoints are affected by the changes in this function?

Or, if a particular logic feels complex, you can request an explanation:

Explain exactly what pattern this regular expression validates.

This approach is much faster and more accurate than manually tracing code. Reviewers can focus on more critical issues, such as the validity of the business logic or edge case handling, instead of superficial aspects. Since questions and answers occur like a real-time conversation rather than asynchronous comments, reviews proceed seamlessly.

Points to Note

Of course, AI responses are not always perfect. Sometimes, it might misinterpret recent changes or miss subtle business contexts. Therefore, AI-generated summaries or analyses should be treated as a ‘draft’ to aid the review, and the final judgment must always be made by the reviewer.

While AI is good at understanding the ‘syntactic’ meaning and structural relationships of code, it struggles to grasp the ‘intent’ behind why the code was written in a particular way. Thus, you’ll get better answers by asking code-based questions like “What is the time complexity of this function?” rather than questions such as “Does this logic comply with our company’s X policy?”

Conclusion

AI-powered editors like Cursor are doing more than just auto-completing code; they are transforming how developers communicate. Their impact is particularly noticeable in code review processes, where context gathering typically consumes significant time. I recommend improving your workflow by using AI to automate repetitive analysis tasks, allowing people to focus on more creative and critical problems.

#

References

• Cursor
• Cursor Docs