Code Review Agent

An AI-powered code review agent that analyzes pull requests for bugs, security vulnerabilities, performance issues, and style consistency.

Developer Tools Security CI/CD

Sample Document

Agent Overview
Review Criteria
CI/CD Integration
Sample Review Output

1. Agent Overview

The Code Review Agent integrates into your GitHub/GitLab CI pipeline to automatically review every pull request. It identifies bugs, security vulnerabilities, performance issues, and deviations from your team's coding standards.

Metric	Value
Languages Supported	Python, JavaScript/TypeScript, Go, Java, Rust, C++
Avg. Review Time	45 seconds per PR
Bug Detection Rate	82% of critical bugs caught
False Positive Rate	< 8%
Security Vulnerability Detection	OWASP Top 10 coverage

2. Review Criteria

Category	Checks	Severity
Security	SQL injection, XSS, CSRF, hardcoded secrets, insecure deserialization	Critical
Bugs	Null pointer dereference, race conditions, resource leaks, off-by-one errors	High
Performance	N+1 queries, unnecessary allocations, blocking I/O, missing indexes	Medium
Style	Naming conventions, function length, cyclomatic complexity, dead code	Low
Architecture	SOLID principles, dependency injection, separation of concerns	Medium
Testing	Test coverage gaps, missing edge cases, assertion quality	Medium

3. CI/CD Integration

GitHub Actions Setup

# .github/workflows/ai-review.yml
name: AI Code Review
on: [pull_request]
jobs:
  review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: industryai/code-review-agent@v2
        with:
          api-key: ${{ secrets.REVIEW_AGENT_KEY }}
          severity-threshold: medium
          auto-approve: false
          languages: python,typescript
          custom-rules: .review-rules.yml

Configuration

Setting	Default	Description
severity-threshold	medium	Minimum severity to report
auto-approve	false	Auto-approve PRs with no issues
max-files	50	Max files to review per PR
ignore-patterns	.test., .spec.	Files to skip
custom-rules	none	Path to team-specific rules

4. Sample Review Output

Example PR Review Comment

## AI Code Review Summary

**Files reviewed:** 8 | **Issues found:** 3 | **Suggestions:** 5

### Critical Issues

**[SECURITY] SQL Injection in user_search.py:42**
The query uses string formatting instead of parameterized queries:
```python
# Current (vulnerable)
cursor.execute(f"SELECT * FROM users WHERE name = '{name}'")

# Suggested fix
cursor.execute("SELECT * FROM users WHERE name = %s", [name])
```

### Performance Issues

**[PERF] N+1 Query in views.py:128**
The loop fetches related objects individually. Use `select_related()`:
```python
# Current (N+1 queries)
for order in Order.objects.all():
    print(order.customer.name)

# Suggested fix
for order in Order.objects.select_related('customer'):
    print(order.customer.name)
```

### Suggestions
- Consider adding type hints to `process_data()` (line 67)
- Function `handle_request` has cyclomatic complexity of 12 (threshold: 10)