Hawkes Smash - AI Innovation Blog

Transforming Code Review with AI

Every development team knows the pain: pull requests piling up, senior developers spending hours reviewing code instead of building features, and junior developers waiting days for feedback. We solved this for a rapidly scaling tech unicorn by building an AI-powered code review assistant.

The Problem

The client, a tech unicorn with 200+ developers, was facing:

Average PR review time: 48-72 hours
Senior developer time: 40% spent on code reviews
Inconsistent standards: Different reviewers, different feedback
Knowledge silos: Domain expertise trapped with specific individuals

The Solution

We built an intelligent code review bot that integrates seamlessly with GitHub, providing instant, consistent, and comprehensive code reviews.

How It Works

Developer opens a PR →
GitHub Action triggers →
AI analyzes changes →
Posts detailed review →
Tracks resolution

Core Features

1. Intelligent Bug Detection

The system catches bugs that often slip through human review:

# Example: AI catches resource leak
def process_file(filename):
    file = open(filename, 'r')  # AI: "Resource leak detected
    data = file.read()           #      File handle not closed
    return process(data)         #      Use context manager instead"

AI suggests:

def process_file(filename):
    with open(filename, 'r') as file:
        data = file.read()
        return process(data)

2. Architecture & Design Review

Beyond syntax, the AI evaluates architectural decisions:

// AI Review Comment:
// "This component has 15 props, violating the single responsibility principle.
//  Consider splitting into smaller components or using composition pattern.
//  
//  Suggested refactor:
//  1. Extract form logic into useFormHandler hook
//  2. Move validation to separate utility
//  3. Use context for shared state instead of prop drilling"

3. Security Vulnerability Scanning

Automated detection of security issues:

# AI detects SQL injection vulnerability
def get_user(user_id):
    query = f"SELECT * FROM users WHERE id = {user_id}"  # Vulnerable!
    
# AI suggests parameterized query:
def get_user(user_id):
    query = "SELECT * FROM users WHERE id = %s"
    cursor.execute(query, (user_id,))

4. Performance Optimization Suggestions

The AI identifies performance bottlenecks:

// AI: "N+1 query detected in this GraphQL resolver.
//      Current implementation makes separate DB call for each item.
//      Use DataLoader pattern for batching."

// Before
async resolve(parent, args, context) {
  const posts = await Post.findAll();
  return posts.map(post => ({
    ...post,
    author: await User.findById(post.authorId) // N+1 problem
  }));
}

// AI suggested improvement
async resolve(parent, args, context) {
  const posts = await Post.findAll();
  const authorIds = posts.map(p => p.authorId);
  const authors = await User.findByIds(authorIds);
  const authorMap = new Map(authors.map(a => [a.id, a]));
  
  return posts.map(post => ({
    ...post,
    author: authorMap.get(post.authorId)
  }));
}

Implementation Details

GitHub Action Workflow

name: AI Code Review

on:
  pull_request:
    types: [opened, synchronize]

jobs:
  ai-review:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      
      - name: Fetch PR diff
        run: |
          git fetch origin ${{ github.base_ref }}
          git diff origin/${{ github.base_ref }}...HEAD > pr.diff
      
      - name: Run AI Review
        run: |
          python review.py \
            --diff pr.diff \
            --pr ${{ github.event.pull_request.number }} \
            --repo ${{ github.repository }}
      
      - name: Post Review Comments
        uses: actions/github-script@v6
        with:
          script: |
            const reviews = require('./review-output.json');
            await postReviewComments(github, context, reviews);

AI Review Engine

class CodeReviewer:
    def __init__(self):
        self.claude = anthropic.Client(api_key=os.getenv('CLAUDE_API_KEY'))
        self.context_builder = ContextBuilder()
        self.comment_formatter = CommentFormatter()
    
    def review_pr(self, diff: str, metadata: dict) -> List[Review]:
        # Build context with relevant files and history
        context = self.context_builder.build(diff, metadata)
        
        # Create specialized prompts for different aspects
        reviews = []
        
        # Security review
        security_review = self.security_review(context)
        reviews.extend(security_review)
        
        # Performance review
        perf_review = self.performance_review(context)
        reviews.extend(perf_review)
        
        # Best practices review
        practices_review = self.best_practices_review(context)
        reviews.extend(practices_review)
        
        # Architecture review for large changes
        if self.is_architectural_change(diff):
            arch_review = self.architecture_review(context)
            reviews.extend(arch_review)
        
        return self.deduplicate_and_prioritize(reviews)

Custom Rulesets

We implemented configurable rulesets per repository:

# .ai-review.yml
enabled: true
  
rules:
  security:
    severity: high
    checks:
      - sql_injection
      - xss_vulnerabilities
      - exposed_secrets
      - insecure_dependencies
  
  performance:
    severity: medium
    checks:
      - n_plus_one_queries
      - unnecessary_loops
      - memory_leaks
      - inefficient_algorithms
  
  code_quality:
    severity: low
    max_complexity: 10
    max_function_length: 50
    require_tests: true
  
ignore_patterns:
  - "*.generated.ts"
  - "migrations/*"
  - "vendor/*"

Results & Impact

Quantitative Metrics

45% reduction in PR review time (48 hours → 26 hours)
3x more issues caught before production
60% reduction in post-deployment bugs
Senior developer time freed up: 15 hours/week per developer

Review Quality Improvements

| Metric | Before AI | After AI | Improvement | |--------|-----------|----------|-------------| | Bugs caught | 2.3/PR | 7.1/PR | 209% ↑ | | Security issues | 0.4/PR | 2.8/PR | 600% ↑ | | Performance issues | 1.1/PR | 4.2/PR | 282% ↑ | | Style consistency | 65% | 94% | 45% ↑ |

Developer Satisfaction

Survey results from 200+ developers:

92% found AI reviews helpful
88% learned something new from AI suggestions
76% reported faster development cycles
95% wanted to keep the system

Challenges Overcome

1. Context Window Limitations

Problem: Large PRs exceeded token limits.

Solution: Intelligent chunking and summarization:

def chunk_large_pr(diff: str, max_tokens: int = 8000):
    chunks = []
    current_chunk = []
    current_tokens = 0
    
    for file_diff in parse_diff(diff):
        file_tokens = count_tokens(file_diff)
        
        if current_tokens + file_tokens > max_tokens:
            chunks.append(summarize_chunk(current_chunk))
            current_chunk = [file_diff]
            current_tokens = file_tokens
        else:
            current_chunk.append(file_diff)
            current_tokens += file_tokens
    
    return chunks

2. False Positives

Problem: AI sometimes flagged non-issues.

Solution: Implemented confidence scoring and feedback loop:

def filter_reviews(reviews: List[Review]) -> List[Review]:
    filtered = []
    for review in reviews:
        # Skip low-confidence suggestions
        if review.confidence < 0.7:
            continue
        
        # Check against historical feedback
        if was_previously_dismissed(review):
            continue
        
        filtered.append(review)
    
    return filtered

3. Integration with Existing Workflows

Problem: Developers had established review processes.

Solution: Made AI assistant complementary, not replacement:

AI reviews marked as "suggestions"
Human approval still required
Ability to dismiss AI comments
Learning from human reviewer corrections

Advanced Features

1. Learning from Feedback

The system improves over time by learning from developer responses:

def learn_from_feedback(pr_number: int, feedback: dict):
    # Store feedback
    db.store_feedback(pr_number, feedback)
    
    # Update model prompts based on patterns
    if feedback['type'] == 'false_positive':
        update_false_positive_filters(feedback)
    elif feedback['type'] == 'missed_issue':
        enhance_detection_prompts(feedback)

2. Custom Team Standards

Teams can define their own standards:

// team-standards.js
module.exports = {
  naming: {
    components: /^[A-Z][a-zA-Z]*$/,
    hooks: /^use[A-Z][a-zA-Z]*$/,
    utilities: /^[a-z][a-zA-Z]*$/
  },
  complexity: {
    max_cyclomatic: 10,
    max_cognitive: 15
  },
  testing: {
    min_coverage: 80,
    require_integration_tests: true
  }
}

3. PR Summary Generation

Automatic generation of PR descriptions:

## Summary
This PR refactors the authentication system to use JWT tokens instead of sessions.

## Changes
- ✨ Implemented JWT token generation and validation
- 🔧 Updated middleware to check JWT tokens
- 🗑️ Removed session-based authentication
- ✅ Added comprehensive tests for JWT flow
- 📝 Updated API documentation

## Impact
- **Performance**: 20% faster auth checks (no DB lookup)
- **Scalability**: Stateless authentication enables horizontal scaling
- **Security**: Tokens expire after 1 hour with refresh mechanism

## Testing
- Unit tests: 42 added, all passing
- Integration tests: Updated, all passing
- Manual testing: Completed on staging

Lessons Learned

1. Start with High-Value, Low-Risk Reviews

We began with style and formatting issues before moving to architectural suggestions.

2. Make It Educational

The best AI reviews teach developers, not just point out issues.

3. Respect Developer Autonomy

AI suggests, humans decide. This principle was key to adoption.

4. Continuous Calibration

Regular tuning based on feedback kept the system relevant and accurate.

Future Roadmap

IDE Integration: Real-time suggestions while coding
Test Generation: Automatic test creation for new code
Refactoring Assistance: Automated refactoring PRs
Cross-repo Learning: Learning patterns across all company repositories

Conclusion

The AI Code Review Assistant transformed how this tech unicorn approaches code quality. By automating the routine aspects of code review, we freed senior developers to focus on architecture and mentoring while actually improving the quality and consistency of reviews.

The key to success wasn't replacing human reviewers but augmenting them with AI that handles the repetitive, pattern-based aspects of review, leaving humans to focus on creativity, context, and complex decision-making.

This system is now reviewing 1,000+ PRs daily, has caught 10,000+ bugs before production, and has become an indispensable part of the development workflow.

AI Code Review Assistant for Tech Unicorn