AI-Assisted Code Review and CI/CD

Introduction

AI-assisted code review can reduce the amount of repetitive work required in a software delivery pipeline, but it should complement engineering review rather than replace it.

A useful pipeline is:

Pull Request
    ↓
Build
    ↓
Tests
    ↓
Static Analysis
    ↓
AI Review
    ↓
Human Review
    ↓
Merge

The AI stage can identify potential issues, summarize changes, and suggest improvements.

What AI Code Review Is Good At

AI can help identify:

• suspicious patterns
• missing validation
• duplicated logic
• unclear naming
• potential nullability problems
• missing tests
• obvious security concerns
• inconsistent implementation patterns

It can also summarize a large pull request for reviewers.

These are recommendations, not proof that the code is correct or incorrect.

Keep Deterministic Checks First

Traditional tools remain important.

For a .NET repository, a pipeline might include:

dotnet restore
dotnet build
dotnet test
static analysis
security scanning
AI review

Deterministic tools should handle deterministic checks where possible.

AI is most useful for tasks that require interpretation.

Pull Request Integration

A practical workflow is:

Developer
   ↓
Pull Request
   ↓
CI Pipeline
   ↓
Collect diff + context
   ↓
AI Review
   ↓
Structured findings
   ↓
Developer / Reviewer

The AI should receive only the context required for the review.

Structured Findings

Ask the AI to produce structured findings rather than unrestricted prose.

For example:

{
  "severity": "warning",
  "file": "OrderService.cs",
  "line": 42,
  "category": "Validation",
  "message": "The request is used before validating the customer identifier.",
  "recommendation": "Validate the identifier before executing the database query."
}

Validate the generated structure before displaying or processing it.

Avoid Blocking Builds on Every AI Finding

AI output is probabilistic.

Treating every model suggestion as a hard CI failure can create noisy pipelines.

A better approach is to classify findings:

Informational → review
Warning       → review
High confidence security finding → potentially block

The exact policy should be based on false-positive rates and organizational risk.

Security

AI code-review systems may process source code, configuration, and pull-request content.

Consider:

• where source code is sent
• provider data retention
• secret redaction
• access controls
• repository permissions
• prompt injection through source files

Never include secrets merely because they happen to be present in a diff.

AI Review Is Not a Security Scanner

An LLM can notice security problems, but it should not replace:

• SAST
• dependency scanning
• secret scanning
• container scanning
• DAST where appropriate

Use specialized tools for specialized guarantees.

.NET Example Workflow

A CI pipeline can conceptually be:

Checkout
   ↓
Restore
   ↓
Build
   ↓
Unit Tests
   ↓
Integration Tests
   ↓
Security / Dependency Scan
   ↓
AI Review
   ↓
Publish Review Findings

The AI step can consume the diff and selected repository context after deterministic checks succeed.

Measuring Usefulness

Evaluate an AI review system using real outcomes:

• accepted findings
• false positives
• missed defects
• review time
• developer feedback
• security findings

Do not claim that AI automatically improves code quality without evidence.

Human Review Remains Important

AI does not understand every business invariant.

A reviewer may know:

• why a workflow exists
• which compatibility constraints matter
• which regulatory requirements apply
• whether a behavior is intentional
• what architectural trade-offs the team has accepted

AI can accelerate review, but ownership remains with the engineering team.

Conclusion

AI-assisted code review works best as another layer in a broader CI/CD system.

Use deterministic tools for deterministic checks, AI for contextual analysis and summarization, and human reviewers for business and architectural judgment.

The goal is not to replace reviewers. It is to help them spend more time on the decisions that require engineering judgment.