Wendy Breiding offers a practical guide to reviewing AI-generated .NET code, discussing how developers can maintain quality and enforce standards in projects that leverage GitHub Copilot and AI code assistants.

Reviewing AI-Generated Code in .NET: Best Practices for Developers

Effective code review is crucial when integrating AI-generated code into .NET projects, especially as tools like GitHub Copilot become more common. Developers play a key role in ensuring that such contributions meet quality, reliability, and maintainability standards.

Why Code Review Matters for AI-Generated Code

As AI tools accelerate feature delivery (with observed gains of 20–40%), diligent code review ensures long-term quality. Reviewers can catch edge cases, standard deviations, and architectural mismatches before they cause tech debt or maintenance headaches.

Key Areas to Watch When Reviewing AI Code

1. API Design & Interface Architecture

  • Interface Abstraction: AI may generate unnecessary interfaces. Check for simplicity.
  • Method Naming: Ensure names meet project standards (e.g., consistent suffixes).
  • API Surface: Limit exposed methods to what’s necessary.
  • Extension Methods: Confirm builder patterns and extensions follow established conventions.

2. Testing & Testability

  • Unit Test Coverage: Insist on comprehensive tests for all new public methods.
  • Test Quality: Use precise assertions and prefer snapshot tests over generic ones.
  • Preserve Existing Tests: Avoid needless changes to previously working tests.

3. File Organization & Architecture

  • Auto-generated Files: Monitor for accidental edits to auto-generated files.
  • Layer Separation: Validate logical placement of code (Infrastructure, Publishing, etc.).
  • Namespaces: Ensure new additions are properly organized into assemblies.

4. Error Handling & Edge Cases

  • Null Checking: Enforce consistent patterns.
  • Exception Handling: Prefer specific exception types over generic ones.
  • Edge Case Coverage: Be vigilant for overlooked scenarios.

5. Configuration & Resource Management

  • Resource Lifecycle: Confirm correct creation/disposal, especially for resources like Docker environments.
  • Configuration Patterns: Adhere to standard callbacks and configuration approaches.
  • Environment Logic: Check for appropriate branching paths (e.g., publish vs run).

6. Code Quality & Standards

  • Documentation: Often missing in AI code—ensure public APIs are well-documented.
  • Styling: Correct formatting or stylistic inconsistencies.
  • Performance: Critically assess design choices that impact performance.

Strategies for Reviewing AI-Generated Pull Requests

  • Iterative Review: Expect more review cycles and incremental edits.
  • Architectural Guidance: Provide strong feedback to align new code with existing designs.
  • Enforce Standards: Insist on team conventions, not generic defaults.
  • Quality Over Quantity: Focus on robust tests, maintainable code, and meaningful documentation.
  • Smaller PRs: Encourage focused, manageable changes for easier review and integration.

Conclusion: The Developer as an AI Code Reviewer

Stepping into the reviewer role for AI-generated code lets you uphold quality as your team adopts modern development tooling. By applying these strategies, you safeguard your codebase while unlocking the productivity that AI tools promise.

References:

This post appeared first on “Microsoft .NET Blog”. Read the entire article here