Code Review Best Practices at FAANG Companies (2025)

These tables capture the main points and trends in code review practices at FAANG companies as of 2025, highlighting the integration of AI, automation, security, and company-specific approaches.

General Code Review Best Practices (2025)

Serial No.	Best Practice	Description (2025)
1	Understand Importance of Code Reviews	Essential for high code quality, early bug detection, knowledge sharing; focus on continuous improvement over perfection.
2	Conduct Thorough Yet Efficient Reviews	Prioritize core logic and architecture; use automation for style/routine checks; reviewers should have relevant expertise.
3	Focus on Enhanced Key Areas	Emphasize security (auth/data), AI-assisted analysis, automated security scans, performance, scalability, and infrastructure-as-code reviews.
4	Provide AI-Enhanced Constructive Feedback	Use AI tools for context-aware suggestions and automated feedback; combine AI with human judgment for best results.
5	Leverage Automation for Speed and Quality	Employ automated tools (static analysis, code standards enforcement, instant checks); modern tools show high user satisfaction and reduced outdated rates.
6	Continuous Learning with Data-Driven Metrics	Use data flywheel: user feedback, monitor outdated rates, daily accuracy labeling, and AI tools that adapt to team patterns.

New Additions and Trends in 2025

Practice/Trend	Details
AI-Human Hybrid Workflow	AI provides initial analysis; humans focus on complex decisions. GitHub Copilot offers instant AI feedback while waiting for human review.
Security-First Review Culture	Security reviews are prioritized; Security Level Agreements (SLAs) define vulnerability detection and resolution times; focus on OWASP Top 10 vulnerabilities.
Multi-Model AI Integration	Use of multiple AI models (e.g., Claude 3.7 Sonnet, OpenAI o1, Google Gemini 2.0 Flash) for specialized review tasks.
Real-Time Collaborative Review	Platforms support real-time sessions, AI PR summaries, chat-based discussions, and instant feedback loops to minimize delays.

Company-Specific Practices (2025)

Company	Distinct Practices
Google	Continuous improvement over perfection, data-driven decisions, escalation for conflicts, lightweight/fast AI-assisted reviews.
Meta/Facebook	AI-driven testing, shift-left approach, automated regression testing, feature flags, dark launches, canary deployments for production testing.
Amazon	Enhanced CodeGuru Security, security-focused best practices, integrated automation for review orchestration.
ByteDance/TikTok	BitsAI-CR system (75% accuracy), two-stage architecture (RuleChecker, ReviewFilter), metric tracking (Outdated Rate).
Apple	Uses iOS-specific tools (SwiftLint, Danger) for code review.
Netflix	Infrastructure-as-code review practices for cloud-native architecture.

Modern Tools and Technologies (2025)

Tool/Platform	Purpose/Features
GitHub Copilot Code Review	AI-powered, integrated into GitHub, provides instant suggestions and one-click fixes.
Codacy	Automated quality gates, supports 40+ languages.
DeepCode by Snyk	Security-focused AI code analysis.
Amazon CodeWhisperer	AWS-integrated code assistance.
Bito's AI Code Review Agent	Context-aware recommendations, incremental review.
CodeAnt AI	SOC2/HIPAA compliant, reduces review time by 50%.
SonarQube	Enhanced security vulnerability detection.
Danger	Automated enforcement of routine review rules.

Key Challenges and Considerations (2025)

Challenge	Description
AI Tool Integration	Balancing AI automation with human expertise.
Security Compliance	Meeting stricter regulatory and security requirements.
Scale Management	Handling reviews across very large developer teams.
Tool Fragmentation	Managing multiple specialized review tools.
False Positive Reduction	Improving AI accuracy to minimize unnecessary alerts.

Security-Focused Practices

Aspect	Practice
Security Reviews	Prioritize high-risk code (auth, data), early SAST/SCA tool integration, focus on OWASP Top 10 vulnerabilities.
SLAs	Define detection and resolution times for vulnerabilities.
Automated Scans	Integrate automated security scans into code review pipelines.

AI & Automation Metrics

Metric/Feature	Value/Impact
BitsAI-CR Review Accuracy	75%
Outdated Rate (BitsAI-CR)	26.7%
User Satisfaction	74.5%
CodeAnt AI Review Time	50% reduction