How to close pull requests faster with Copilot code review | GitHub Checkout

Quick Overview

GitHub Copilot Code Review can close pull requests 43% faster by automating repetitive review tasks, adding context for reviewers, and integrating static analysis tools like CodeQL, resulting in increased user retention and quality improvements.

Key Points: GitHub Copilot Code Review accelerates PR closure by 43% for users who adopt it as part of their review process. The feature automates repetitive review tasks, adds contextual information for reviewers, and integrates tools like CodeQL and ESLint. The tool provides a PR Overview summarizing key changes, which helps reviewers quickly understand the pull request content. Custom repository instructions can be leveraged to guide the AI reviewer's focus and ensure reviews align with organizational standards. GitHub Actions workflows dynamically orchestrate the review process, using an agent to pull necessary context and run various analysis tools in parallel. Early user feedback shows increased retention and massive quality improvements in reviews through this automated assistance.

Context: This video features Elle Shwer, Senior Product Manager at GitHub, discussing the benefits and implementation of the newly announced GitHub Copilot Code Review feature, which is currently in public preview at Universe. The discussion focuses on how this AI assistance streamlines the code review process for developers, especially in enterprise and business settings.

Detailed Analysis

Elle Shwer details how GitHub Copilot Code Review speeds up PR closure by 43% for users who integrate it into their workflow. This acceleration comes from automating repetitive review steps, providing contextual summaries, and integrating static analysis tools like CodeQL and ESLint. The process involves setting up a custom GitHub Action workflow that dynamically orchestrates the review, using an agent to fetch necessary context like PR files and run analyses. Shwer shows an example PR where Copilot automatically requested a review, provided a summary of key changes, and suggested fixes for issues like a missing 'await' keyword before an async function call. Furthermore, repository-level custom instructions allow organizations to tailor the AI's review focus to their specific coding standards and quality expectations, ensuring consistency across all reviews, even for specialized security tools like CodeQL. The positive feedback from early users indicates improved retention and significant quality gains in the review process.