AIReview in Practice: Accelerating Code Reviews with AI

GitHub: https://github.com/wosledon/AIReview

Tired of slow reviews, inconsistent quality, fragmented discussions, and repetitive work? This post shares how AIReview brings multi-LLM power, customizable prompts, async analysis, real-time collaboration, and Git integration together to make code reviews faster, more consistent, and genuinely actionable.

What Problems Are We Solving?

• Low efficiency: Large PRs with many changes are time-consuming to read and easy to miss risks.
• Inconsistent standards: Different reviewers apply different lenses; feedback scatters across chats and comments with little reuse.
• Unstructured feedback: Findings aren’t organized by dimensions, making it hard to align team standards and track improvements over time.
• Repetitive chores: Boilerplate checks and narratives (PR summary, risk hints, testing advice) are typed over and over.

AIReview automates what can be automated and structures what needs human judgment, so reviews are both faster and more accurate.

A Quick Tour: From Project to Review

A few screenshots to give you a feel for the end-to-end experience:

Key Capabilities at a Glance

1) Intelligent AI Code Review (multi-dimensional)

• Risk scoring across quality, security, performance, and maintainability
• Context-aware suggestions that focus on actionable improvements
• Multi-LLM support (OpenAI, Azure OpenAI, private models), configurable per project/user
• Async background analysis (Hangfire) keeps UX snappy for large codebases

2) Customizable Prompts (3-tier templates)

• Built-in → User-level → Project-level overrides to match team standards
• Placeholders like {{CONTEXT}}, {{DIFF}}, {{FILE_NAME}}
• Visual UI to create/update/delete templates

3) Advanced PR Analysis and Summaries

• Auto-generated change summaries, impact assessments, deployment notes, and rollback hints
• Change type classification (feature, fix, refactor, docs, etc.)
• Testing recommendations and focus areas so reviewers waste less time

4) Improvement Suggestions Engine

• Suggestions categorized by quality, performance, security, architecture, etc.
• Priority scoring (impact × effort) to plan work effectively
• Accept/ignore feedback loop to track adoption and trends

5) Deep Git Integration

• Import existing repositories, parse diffs, and bind to commit history
• Multi-branch workflows with review records

6) Real-time Collaboration and Workflow

• SignalR notifications for instant updates on comments and status
• Full lifecycle: request → assign → approve/reject/request changes

7) Observability and Cost Awareness

• Token usage and call statistics (TokenUsage API) to measure and optimize cost

A Smooth Review Flow (0 → 1)

1. Create a project and configure LLMs and prompt templates

   • Tailor templates to your team’s language and standards.

2. Link/import a Git repo and trigger analysis

   • Start a review for a PR or branch; the system fetches the diff and runs async analysis.

3. Start with the “Review Home”

   • Read the auto-summary and risk scores; jump straight to high-signal files and hunks.

4. Dive into file/line views

   • Review AI suggestions and evidence (context/snippets); add human judgment and team conventions.

5. Drive clear outcomes

   • Use comments and todo items to capture fixes; approve, reject, or request changes as needed.

6. Feed learning back into templates

   • Turn new agreements from discussions into templates so they apply automatically next time.

Architecture and Tech (Short Version)

• Clear layering and domains: API (ASP.NET Core) / Core (domain & business) / Infrastructure (EF Core, external services, Hangfire, Redis)
• Real-time messaging via SignalR
• Databases: SQLite (default) or PostgreSQL (recommended for production)
• Frontend: React + TypeScript + Vite + TailwindCSS + React Query
• Async processing for long-running AI analysis via Hangfire
• Repository + Unit of Work, interface-driven to swap LLMs/integrations easily

Further reading:

• Chinese design doc: docs/design.md
• English design doc: docs/design.en-us.md

Getting Started (Dev Environment)

Backend (.NET 8):

• Configure AIReview.API/appsettings.Development.json (connection strings, JWT, optional Redis)
• Run migrations and start the API (Swagger available)

Frontend (React + Vite):

• Install deps and set VITE_API_BASE_URL
• Start the dev server and open in a browser

Tip: AIReview.Tests helps verify backend logic; hook up Vitest/Jest on the frontend as needed.

Why Teams Like It

• More accurate: multi-model + customizable templates that reflect your codebase and language
• Faster: async analysis + caching helps you quickly find high-risk areas in big PRs
• More controllable: token usage is trackable, analysis granularity is tunable, costs are measurable
• More collaborative: real-time comments and a unified workflow reduce fragmentation
• More extensible: interface-driven architecture to add new LLMs or enterprise integrations

Roadmap (Highlights)

• Smarter “code fix suggestions”: generate previewable patches and diffs
• Multi-model aggregation: combine LLMs for robustness
• IDE integration: VS Code extension for inline reviews
• Analytics dashboards: quality trends, team efficiency, and technical debt
• Security/compliance: deeper security scans and license checks

Find the full roadmap and features in the root README.md and the docs/ folder.

Closing Thoughts

AI isn’t here to replace reviewers—it augments human judgment and impact. Let machines handle the repetitive parts so people can focus on choices that require experience and consensus. Try AIReview, and share your team’s practices—we’d love to make “AI-era code reviews” better together.

• Source and issues: https://github.com/wosledon/AIReview (Issues and Discussions)
• License: MIT (see root LICENSE)

If you find the project or this post useful, a star on the repo would mean a lot!