Authored by Sneha Tuli, this article details how Microsoft’s AI-powered code review assistant revolutionizes pull request workflows, boosts code quality, and shapes GitHub Copilot’s code review process to benefit developers worldwide.

Enhancing Code Quality at Scale with AI-Powered Code Reviews

Author: Sneha Tuli

Introduction

At Microsoft, ongoing efforts to improve developer productivity and code quality have led to the development of an AI-powered code review assistant. Initially deployed internally, this assistant now reviews over 90% of Microsoft’s pull requests, impacting more than 600,000 PRs monthly. The tool focuses on accelerating PR completion, promoting consistent best practices, and providing actionable feedback directly within established workflows.

Built in partnership with Microsoft’s Developer Division Data & AI team, the solution’s internal learnings were key to shaping GitHub’s AI-powered code review, benefiting the broader developer ecosystem. The feedback loop between internal and external usage continues to drive improvements.

Addressing PR Review Challenges With AI

Pull requests (PRs) are essential for collaborative development, but traditional review processes often have friction:

Reviewers spend time on low-value issues (e.g., style inconsistencies).
Important feedback (e.g., architectural or security concerns) can be missed or delayed.
Large-scale development means some PRs wait too long for review or miss critical comments.

AI Reviewer Integration

The AI code reviewer integrates with standard workflows and is triggered automatically for each PR. Key features include:

Automated Checks and Comments: The AI reviews code changes, flags issues (from style errors to potential bugs or inefficiencies), categorizes suggestions (e.g., exception handling, sensitive data), and posts comments within the PR thread for visibility and action.
Suggested Improvements: The assistant proposes specific code changes or alternative implementations without making direct commits. Developers review, edit, and accept changes, maintaining transparency and accountability via commit history.
PR Summary Generation: AI constructs summaries describing the purpose and highlights of a PR, helping reviewers quickly grasp the intent and scope without manual analysis.
Interactive Q&A (“Ask the AI”): Reviewers can ask the AI clarifying questions about the code (e.g., parameter usage, module impact), leveraging it as a conversational assistant.

The smooth integration and interaction—without requiring new UI or tools—have contributed to widespread adoption. The AI acts as an always-available, first-pass reviewer present from the moment a PR is created.

Impact on Quality and Development Velocity

Adoption results:

Faster Review Cycles: PR completion times improved by 10–20% in early experiments with 5,000+ repositories. Authors receive suggested fixes within minutes, reducing iteration cycles.
Improved Code Quality: The AI consistently enforces standards and spots subtle bugs (such as missing null checks or out-of-order API calls) before code is merged.
Developer Learning: Particularly for onboarding new hires, the AI guides best practices and reviews every line like a mentor.

Customization & Extensibility

Teams can extend the assistant for repository-specific guidelines and custom review prompts, such as:

Detecting regressions from historical patterns
Enforcing specific change gates or compliance policies

This flexibility supports specialized reviews tailored to diverse team needs.

From Internal Innovation to Community Impact

Microsoft’s early, large-scale adoption enabled rapid iteration and validation of AI-assisted reviews. Experiences shaped central features like inline suggestions and human-in-the-loop workflows, influencing GitHub Copilot for Pull Request Reviews, which became generally available in April 2025.

Insights and usage data flow bidirectionally: features adopted in GitHub Copilot feed improvements back into Microsoft’s internal workflows, ensuring co-evolution and aligning innovation across the developer community.

Looking Forward

AI-powered code reviews are transforming review processes at scale. Microsoft’s focus continues to be on deepening contextual understanding, referencing past PRs, and learning from human patterns for even more tailored, team-specific review experiences.

The ultimate vision: AI handles routine checks, while human reviewers focus on high-value feedback, increasing both speed and confidence in shipping high-quality code.

Developers everywhere—from Microsoft to the open-source community—can benefit by integrating AI-powered review tools like GitHub Copilot’s code review into their workflows.

Content adapted from Engineering@Microsoft.

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