AI Code Review and Validation
Overview
AI can assist code review by identifying obvious smells, missing null checks, or repetitive defects. Final approval should still depend on deterministic checks and human engineering review.
Validation Layers
flowchart LR
A[AI Draft] --> B[Static Analysis]
B --> C[Unit Tests]
C --> D[Integration Tests]
D --> E[Security Scan]
E --> F[Human Review]
Practical Example - Add Defensive Validation
public sealed class TransferService
{
public Task TransferAsync(decimal amount, string fromAccountId, string toAccountId, CancellationToken ct)
{
if (amount <= 0) throw new ArgumentOutOfRangeException(nameof(amount));
if (string.IsNullOrWhiteSpace(fromAccountId)) throw new ArgumentException("Source account is required.", nameof(fromAccountId));
if (string.IsNullOrWhiteSpace(toAccountId)) throw new ArgumentException("Destination account is required.", nameof(toAccountId));
if (fromAccountId == toAccountId) throw new InvalidOperationException("Source and destination must differ.");
return Task.CompletedTask;
}
}
Production Example - CI Gate Policy
A CI pipeline can block merge when:
- Test coverage drops below threshold
- New critical security findings appear
- Contract tests fail
- AI generated code is not labeled in pull request description
Things to Watch Out For
- Hallucinated package names or methods
- Missing async cancellation token propagation
- Incorrect exception types for API boundaries
Key Takeaways
- AI review is a helper, not a replacement.
- Deterministic quality gates reduce regression risk.
- Contract and security checks are critical for production safety.