Excellent concept! AI-powered DevOps pipelines are revolutionizing how teams ship code. Let me show you how to build a comprehensive automated code review system using Python for AI analysis and Node.js for pipeline integration.
System Architecture Overview
[GitHub/GitLab] --> [Node.js Webhook Handler] --> [Python AI Analyzer]
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| v
[Status Updates] <-- [Node.js Orchestrator] <-- [Review Results & Suggestions]Core Components
1. Node.js Webhook Server & Orchestrator
// server.js - Main webhook handler const express = require('express'); const axios = require('axios'); const crypto = require('crypto'); const { spawn } = require('child_process'); const app = express(); app.use(express.json()); // Configuration const CONFIG = { GITHUB_WEBHOOK_SECRET: process.env.GITHUB_WEBHOOK_SECRET, PYTHON_AI_SERVICE: process.env.PYTHON_AI_SERVICE || 'http://localhost:5000', SUPPORTED_LANGUAGES: ['javascript', 'python', 'typescript', 'java', 'go'] }; // GitHub webhook verification function verifyGitHubSignature(req, res, next) { const signature = req.headers['x-hub-signature-256']; if (!signature) { return res.status(401).json({ error: 'Missing signature' }); } const hmac = crypto.createHmac('sha256', CONFIG.GITHUB_WEBHOOK_SECRET); const digest = `sha256=${hmac.update(JSON.stringify(req.body)).digest('hex')}`; if (!crypto.timingSafeEqual(Buffer.from(signature), Buffer.from(digest))) { return res.status(401).json({ error: 'Invalid signature' }); } next(); } // AI Code Review Orchestrator class CodeReviewOrchestrator { constructor() { this.reviewCache = new Map(); } async performCodeReview(pullRequest) { const { repository, pull_request: pr } = pullRequest; const cacheKey = `${repository.full_name}#${pr.number}`; // Check cache for recent review if (this.reviewCache.has(cacheKey)) { return this.reviewCache.get(cacheKey); } try { // 1. Get PR details and changes const prDetails = await this.getPRDetails(repository, pr.number); const files = await this.getChangedFiles(repository, pr.number); // 2. Filter relevant files const reviewableFiles = files.filter(file => this.isReviewableFile(file.filename) && file.status !== 'removed' ); // 3. Get file contents const fileContents = await this.getFileContents(repository, reviewableFiles); // 4. Send to Python AI service for analysis const aiAnalysis = await this.analyzeWithAI({ files: fileContents, pr_title: pr.title, pr_description: pr.body, repository: repository.full_name, base_branch: pr.base.ref, head_branch: pr.head.ref }); // 5. Post review comments to GitHub await this.postReviewComments(repository, pr.number, aiAnalysis.comments); // 6. Update PR status await this.updatePRStatus(repository, pr.head.sha, aiAnalysis.summary); // Cache the result this.reviewCache.set(cacheKey, aiAnalysis); setTimeout(() => this.reviewCache.delete(cacheKey), 300000); // 5 min cache return aiAnalysis; } catch (error) { console.error('Code review failed:', error); throw error; } } async analyzeWithAI(reviewContext) { try { const response = await axios.post(`${CONFIG.PYTHON_AI_SERVICE}/analyze`, reviewContext, { timeout: 60000 // 60 second timeout for AI analysis }); return response.data; } catch (error) { console.error('AI service error:', error.message); throw new Error(`AI analysis failed: ${error.message}`); } } async getPRDetails(repo, prNumber) { // Implementation for getting PR details from GitHub API const response = await axios.get( `https://api.github.com/repos/${repo.full_name}/pulls/${prNumber}`, { headers: { Authorization: `token ${process.env.GITHUB_TOKEN}` } } ); return response.data; } async getChangedFiles(repo, prNumber) { const response = await axios.get( `https://api.github.com/repos/${repo.full_name}/pulls/${prNumber}/files`, { headers: { Authorization: `token ${process.env.GITHUB_TOKEN}` } } ); return response.data; } async getFileContents(repo, files) { const contents = []; for (const file of files) { try { const response = await axios.get( `https://api.github.com/repos/${repo.full_name}/contents/${file.filename}?ref=${file.contents_url.split('?ref=')[1]}`, { headers: { Authorization: `token ${process.env.GITHUB_TOKEN}` } } ); const content = Buffer.from(response.data.content, 'base64').toString(); contents.push({ filename: file.filename, content: content, changes: file.patch, // The diff patch status: file.status }); } catch (error) { console.warn(`Could not fetch content for ${file.filename}:`, error.message); } } return contents; } async postReviewComments(repo, prNumber, comments) { for (const comment of comments) { await axios.post( `https://api.github.com/repos/${repo.full_name}/pulls/${prNumber}/comments`, { body: this.formatComment(comment), commit_id: comment.commit_id, path: comment.file_path, line: comment.line_number, side: comment.side || 'RIGHT' }, { headers: { Authorization: `token ${process.env.GITHUB_TOKEN}` } } ); // Rate limiting await new Promise(resolve => setTimeout(resolve, 1000)); } } async updatePRStatus(repo, sha, summary) { const state = summary.issues_found > 0 ? 'failure' : 'success'; await axios.post( `https://api.github.com/repos/${repo.full_name}/statuses/${sha}`, { state: state, target_url: process.env.CI_BUILD_URL, description: summary.issues_found > 0 ? `Found ${summary.issues_found} issues needing attention` : 'AI review passed - no critical issues found', context: 'ai-code-review/bot' }, { headers: { Authorization: `token ${process.env.GITHUB_TOKEN}` } } ); } formatComment(comment) { return ` 🤖 **AI Code Review** **${comment.category.toUpperCase()}**: ${comment.title} ${comment.description} **Suggestion**: ${comment.suggestion} **Confidence**: ${(comment.confidence * 100).toFixed(0)}% ${comment.example ? `**Example**: \`\`\`${comment.language}\n${comment.example}\n\`\`\`` : ''} `.trim(); } isReviewableFile(filename) { const extension = filename.split('.').pop(); const supported = ['js', 'ts', 'py', 'java', 'go', 'cpp', 'c', 'rs', 'php']; return supported.includes(extension); } } // Initialize orchestrator const orchestrator = new CodeReviewOrchestrator(); // Webhook endpoint app.post('/webhook/github', verifyGitHubSignature, async (req, res) => { const event = req.headers['x-github-event']; const payload = req.body; // Only process pull request events if (event !== 'pull_request') { return res.status(200).json({ status: 'ignored', reason: 'Not a pull request event' }); } // Only process opened, synchronize, or reopened PRs if (!['opened', 'synchronize', 'reopened'].includes(payload.action)) { return res.status(200).json({ status: 'ignored', reason: 'Action not relevant for review' }); } try { // Process in background setImmediate(async () => { try { await orchestrator.performCodeReview(payload); } catch (error) { console.error('Background processing error:', error); } }); res.status(202).json({ status: 'accepted', message: 'Code review started' }); } catch (error) { console.error('Webhook processing error:', error); res.status(500).json({ error: 'Internal server error' }); } }); // Health check endpoint app.get('/health', (req, res) => { res.json({ status: 'healthy', service: 'ai-code-review-orchestrator', timestamp: new Date().toISOString() }); }); const PORT = process.env.PORT || 3000; app.listen(PORT, () => { console.log(`AI Code Review Orchestrator running on port ${PORT}`); });
2. Python AI Analysis Engine
# ai_analyzer.py - Core AI analysis engine from flask import Flask, request, jsonify import openai from anthropic import Anthropic import os import re import ast import tempfile import subprocess from pathlib import Path from typing import List, Dict, Any import logging from dataclasses import dataclass app = Flask(__name__) # Configuration OPENAI_API_KEY = os.getenv('OPENAI_API_KEY') ANTHROPIC_API_KEY = os.getenv('ANTHROPIC_API_KEY') GITHUB_TOKEN = os.getenv('GITHUB_TOKEN') # Initialize clients openai_client = openai.OpenAI(api_key=OPENAI_API_KEY) if OPENAI_API_KEY else None anthropic_client = Anthropic(api_key=ANTHROPIC_API_KEY) if ANTHROPIC_API_KEY else None @dataclass class CodeIssue: file_path: str line_number: int category: str title: str description: str suggestion: str confidence: float severity: str # low, medium, high, critical example: str = "" class CodeAnalyzer: def __init__(self): self.supported_patterns = { 'security': self._analyze_security, 'performance': self._analyze_performance, 'maintainability': self._analyze_maintainability, 'bug_risk': self._analyze_bug_risk, 'best_practices': self._analyze_best_practices } def analyze_code(self, files: List[Dict], pr_context: Dict) -> Dict[str, Any]: """Main analysis entry point""" all_issues = [] for file in files: file_issues = self._analyze_file(file, pr_context) all_issues.extend(file_issues) # Categorize and summarize summary = self._generate_summary(all_issues) return { 'issues': all_issues, 'summary': summary, 'comments': self._format_for_github(all_issues) } def _analyze_file(self, file: Dict, pr_context: Dict) -> List[CodeIssue]: """Analyze a single file""" issues = [] filename = file['filename'] content = file['content'] changes = file.get('changes', '') # Language-specific analysis file_extension = Path(filename).suffix.lower() # Pattern-based analysis for category, analyzer in self.supported_patterns.items(): category_issues = analyzer(filename, content, changes, file_extension) issues.extend(category_issues) # AI-powered analysis ai_issues = self._ai_analysis(filename, content, changes, pr_context) issues.extend(ai_issues) return issues def _analyze_security(self, filename: str, content: str, changes: str, extension: str) -> List[CodeIssue]: """Security vulnerability analysis""" issues = [] security_patterns = { 'javascript': [ (r'eval\s*\(', 'Use of eval() function', 'high'), (r'localStorage\.setItem\s*\([^)]*password', 'Storing passwords in localStorage', 'critical'), (r'innerHTML\s*=', 'Potential XSS vulnerability', 'high'), (r'https?://[^\s"\']*', 'Hardcoded URLs', 'medium'), ], 'python': [ (r'subprocess\.call|os\.system', 'Shell command injection risk', 'high'), (r'pickle\.loads', 'Unsafe deserialization', 'critical'), (r'exec\(|eval\(', 'Code execution vulnerability', 'high'), (r'password\s*=\s*["\']', 'Hardcoded credentials', 'critical'), ] } lang = 'javascript' if extension in ['.js', '.ts', '.jsx', '.tsx'] else 'python' patterns = security_patterns.get(lang, []) for pattern, description, severity in patterns: matches = re.finditer(pattern, content, re.IGNORECASE) for match in matches: line_number = content[:match.start()].count('\n') + 1 issues.append(CodeIssue( file_path=filename, line_number=line_number, category='security', title=description, description=f"Potential security vulnerability found", suggestion=f"Consider using safer alternatives and validate inputs", confidence=0.8, severity=severity, example=self._get_example_fix(pattern, lang) )) return issues def _analyze_performance(self, filename: str, content: str, changes: str, extension: str) -> List[CodeIssue]: """Performance issue analysis""" issues = [] # Common performance patterns performance_patterns = [ (r'for\s*\([^)]*\)\s*{[\s\S]*?\bfor\s*\([^)]*\)\s*{', 'Nested loops - O(n²) complexity', 'medium'), (r'JSON\.parse\s*\([^)]*\)\s*in\s+loop', 'JSON parsing in loop', 'medium'), (r'document\.querySelectorAll\s*\([^)]*\)\s*\.forEach', 'DOM query in loop', 'medium'), ] for pattern, description, severity in performance_patterns: if re.search(pattern, content, re.MULTILINE): issues.append(CodeIssue( file_path=filename, line_number=1, # Would need more sophisticated line detection category='performance', title=description, description="Performance optimization opportunity", suggestion="Consider optimizing the algorithm or caching results", confidence=0.7, severity=severity )) return issues def _analyze_maintainability(self, filename: str, content: str, changes: str, extension: str) -> List[CodeIssue]: """Code maintainability analysis""" issues = [] # Large function detection functions = self._extract_functions(content, extension) for func_name, func_info in functions.items(): if func_info['line_count'] > 50: issues.append(CodeIssue( file_path=filename, line_number=func_info['start_line'], category='maintainability', title=f"Large function: {func_name} ({func_info['line_count']} lines)", description="Functions should generally be under 50 lines for maintainability", suggestion="Consider breaking this function into smaller, focused functions", confidence=0.9, severity='medium' )) # Complex conditional detection complex_conditionals = re.findall(r'if\s*\([^)]{100,}\)', content) for conditional in complex_conditionals: issues.append(CodeIssue( file_path=filename, line_number=1, category='maintainability', title="Complex conditional expression", description="Conditional logic is complex and hard to maintain", suggestion="Extract complex conditions into well-named variables or functions", confidence=0.8, severity='low' )) return issues def _analyze_bug_risk(self, filename: str, content: str, changes: str, extension: str) -> List[CodeIssue]: """Potential bug detection""" issues = [] bug_patterns = { 'javascript': [ (r'==\s*(null|undefined)', 'Use === for null/undefined checks', 'medium'), (r'console\.log\(', 'Leftover debug statement', 'low'), (r'try\s*{[\s\S]*?}\s*catch\s*\([^)]*\)\s*{}', 'Empty catch block', 'high'), ], 'python': [ (r'except:\s*pass', 'Bare except with pass', 'high'), (r'print\(', 'Leftover debug statement', 'low'), (r'assert\s+[^,)]*$', 'Assert without message', 'medium'), ] } lang = 'javascript' if extension in ['.js', '.ts'] else 'python' patterns = bug_patterns.get(lang, []) for pattern, description, severity in patterns: matches = re.finditer(pattern, content, re.MULTILINE) for match in matches: line_number = content[:match.start()].count('\n') + 1 issues.append(CodeIssue( file_path=filename, line_number=line_number, category='bug_risk', title=description, description="Potential bug or code smell detected", suggestion="Fix the issue to prevent potential bugs", confidence=0.85, severity=severity )) return issues def _analyze_best_practices(self, filename: str, content: str, changes: str, extension: str) -> List[CodeIssue]: """Coding best practices analysis""" issues = [] best_practice_patterns = [ (r'//\s*TODO:', 'TODO comment left in code', 'low'), (r'//\s*FIXME:', 'FIXME comment left in code', 'medium'), (r'function\s+[a-z][a-zA-Z]*', 'Function name should be camelCase', 'low'), ] for pattern, description, severity in best_practice_patterns: if re.search(pattern, content): issues.append(CodeIssue( file_path=filename, line_number=1, category='best_practices', title=description, description="Code style or best practice issue", suggestion="Follow team coding conventions and best practices", confidence=0.9, severity=severity )) return issues def _ai_analysis(self, filename: str, content: str, changes: str, pr_context: Dict) -> List[CodeIssue]: """Use AI models for advanced code analysis""" if not anthropic_client: return [] try: prompt = self._build_analysis_prompt(filename, content, changes, pr_context) response = anthropic_client.messages.create( model="claude-3-sonnet-20240229", max_tokens=1000, temperature=0.1, system="You are an expert code reviewer. Analyze code for issues and provide specific, actionable feedback.", messages=[{"role": "user", "content": prompt}] ) return self._parse_ai_response(response.content[0].text, filename) except Exception as e: logging.error(f"AI analysis failed: {e}") return [] def _build_analysis_prompt(self, filename: str, content: str, changes: str, pr_context: Dict) -> str: """Build prompt for AI analysis""" return f""" Analyze this code file for the pull request: PR Title: {pr_context.get('pr_title', 'N/A')} PR Description: {pr_context.get('pr_description', 'N/A')} Repository: {pr_context.get('repository', 'N/A')} File: {filename} Code Content: ``` {content} ``` Changes in this PR: ``` {changes} ``` Please analyze for: 1. Security vulnerabilities 2. Performance issues 3. Code smells and maintainability 4. Potential bugs 5. Best practices violations 6. Architecture concerns Provide specific, actionable feedback with line numbers if possible. """ def _parse_ai_response(self, response: str, filename: str) -> List[CodeIssue]: """Parse AI response into structured issues""" # This would parse the AI response into CodeIssue objects # Implementation depends on the AI model's response format issues = [] # Simple pattern matching for demonstration lines = response.split('\n') current_issue = None for line in lines: if 'CRITICAL:' in line or 'HIGH:' in line or 'MEDIUM:' in line or 'LOW:' in line: if current_issue: issues.append(current_issue) severity = 'medium' if 'CRITICAL' in line: severity = 'critical' elif 'HIGH' in line: severity = 'high' elif 'LOW' in line: severity = 'low' current_issue = CodeIssue( file_path=filename, line_number=1, category='ai_analysis', title=line.strip(), description="", suggestion="", confidence=0.8, severity=severity ) elif current_issue and line.strip(): if not current_issue.description: current_issue.description = line.strip() else: current_issue.suggestion = line.strip() if current_issue: issues.append(current_issue) return issues def _extract_functions(self, content: str, extension: str) -> Dict[str, Any]: """Extract function information from code""" functions = {} if extension in ['.py']: # Python function extraction try: tree = ast.parse(content) for node in ast.walk(tree): if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)): start_line = node.lineno end_line = node.end_lineno or start_line functions[node.name] = { 'start_line': start_line, 'line_count': end_line - start_line + 1 } except: pass return functions def _get_example_fix(self, pattern: str, language: str) -> str: """Provide example fixes for common issues""" fixes = { 'eval': { 'javascript': '// Instead of eval(expression)\n// Use: const result = safeEval(expression) or a parser', 'python': '# Instead of eval(expression)\n# Use: ast.literal_eval(expression) or a safe parser' }, 'localStorage password': { 'javascript': '// Instead of localStorage.setItem("password", pwd)\n// Use: secure authentication with backend' } } for key, examples in fixes.items(): if key in pattern: return examples.get(language, '') return '' def _generate_summary(self, issues: List[CodeIssue]) -> Dict[str, Any]: """Generate summary of all issues found""" severity_counts = {'critical': 0, 'high': 0, 'medium': 0, 'low': 0} category_counts = {} for issue in issues: severity_counts[issue.severity] += 1 category_counts[issue.category] = category_counts.get(issue.category, 0) + 1 return { 'total_issues': len(issues), 'issues_by_severity': severity_counts, 'issues_by_category': category_counts, 'has_critical_issues': severity_counts['critical'] > 0, 'has_high_issues': severity_counts['high'] > 0, 'review_passed': severity_counts['critical'] == 0 and severity_counts['high'] == 0 } def _format_for_github(self, issues: List[CodeIssue]) -> List[Dict]: """Format issues for GitHub API""" return [ { 'file_path': issue.file_path, 'line_number': issue.line_number, 'category': issue.category, 'title': issue.title, 'description': issue.description, 'suggestion': issue.suggestion, 'confidence': issue.confidence, 'severity': issue.severity, 'example': issue.example } for issue in issues ] # Initialize analyzer analyzer = CodeAnalyzer() @app.route('/analyze', methods=['POST']) def analyze_code(): """Main analysis endpoint""" try: data = request.json files = data.get('files', []) pr_context = { 'pr_title': data.get('pr_title', ''), 'pr_description': data.get('pr_description', ''), 'repository': data.get('repository', ''), 'base_branch': data.get('base_branch', ''), 'head_branch': data.get('head_branch', '') } result = analyzer.analyze_code(files, pr_context) return jsonify(result) except Exception as e: logging.error(f"Analysis error: {e}") return jsonify({ 'error': 'Analysis failed', 'message': str(e) }), 500 @app.route('/health', methods=['GET']) def health_check(): return jsonify({ 'status': 'healthy', 'service': 'ai-code-analyzer', 'ai_available': bool(anthropic_client or openai_client) }) if __name__ == '__main__': app.run(host='0.0.0.0', port=5000, debug=False)
3. Package.json and Dependencies
{ "name": "ai-code-review-devops", "version": "1.0.0", "description": "AI-powered automated code review system", "main": "server.js", "scripts": { "start": "node server.js", "dev": "nodemon server.js", "test": "jest", "lint": "eslint .", "build": "echo 'No build step required'" }, "dependencies": { "express": "^4.18.2", "axios": "^1.6.0", "crypto": "^1.0.1", "dotenv": "^16.3.1", "winston": "^3.11.0", "express-rate-limit": "^7.1.5", "helmet": "^7.1.0", "cors": "^2.8.5" }, "devDependencies": { "nodemon": "^3.0.2", "jest": "^29.7.0", "eslint": "^8.54.0", "supertest": "^6.3.3" }, "engines": { "node": ">=18.0.0" } }
4. Docker Configuration
# Node.js Dockerfile FROM node:18-alpine WORKDIR /app COPY package*.json ./ RUN npm ci --only=production COPY . . USER node EXPOSE 3000 CMD ["node", "server.js"]
# Python Dockerfile FROM python:3.11-slim WORKDIR /app COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt COPY . . USER nobody EXPOSE 5000 CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:5000", "ai_analyzer:app"]
# docker-compose.yml version: '3.8' services: node-orchestrator: build: ./node-orchestrator ports: - "3000:3000" environment: - GITHUB_WEBHOOK_SECRET=${GITHUB_WEBHOOK_SECRET} - GITHUB_TOKEN=${GITHUB_TOKEN} - PYTHON_AI_SERVICE=http://python-analyzer:5000 - NODE_ENV=production depends_on: - python-analyzer python-analyzer: build: ./python-analyzer environment: - OPENAI_API_KEY=${OPENAI_API_KEY} - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY} - GITHUB_TOKEN=${GITHUB_TOKEN} redis: image: redis:alpine ports: - "6379:6379" # Monitoring prometheus: image: prom/prometheus ports: - "9090:9090"
Key Features
Multi-Layer Analysis:
Pattern-based security scanning
Performance optimization detection
Maintainability and complexity analysis
AI-powered contextual review
Smart Integration:
GitHub webhook handling
PR status updates
Inline comment posting
Caching for performance
Production Ready:
Error handling and retries
Rate limiting
Health checks
Comprehensive logging
Setup and Deployment
Environment Variables:
# GitHub Configuration GITHUB_WEBHOOK_SECRET=your_webhook_secret GITHUB_TOKEN=your_github_token # AI Services OPENAI_API_KEY=your_openai_key ANTHROPIC_API_KEY=your_anthropic_key # Deployment NODE_ENV=production PYTHON_AI_SERVICE=http://localhost:5000
Webhook Configuration:
Set up GitHub webhook to point to your Node.js service
Configure for pull request events
Set content type to
application/json
Python Requirements:
flask==2.3.3 openai==1.3.7 anthropic==0.7.4 gunicorn==21.2.0 python-dotenv==1.0.0
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