Architecture Analysis & Mapping
How CodeDD's 3-phase AI system reverse-engineers your software architecture
Architecture Analysis & Mapping
Overview
CodeDD's architecture analysis goes beyond traditional dependency graphing. Using a sophisticated 3-phase AI-powered system, CodeDD reverse-engineers your software architecture to create an interactive visual map of your entire system—including components, technologies, data flows, and relationships.
Why Architecture Analysis Matters
The Challenge
Most codebases lack up-to-date architecture documentation:
- Original design documents become outdated
- Tribal knowledge exists only in developers' heads
- New team members struggle to understand the system
- Investors can't assess architectural risks
- Technical debt accumulates in invisible ways
CodeDD's Solution
Automated Architecture Discovery:
- Analyzes actual code, not documentation
- Identifies all technologies and frameworks in use
- Maps component relationships and data flows
- Detects architectural patterns and anti-patterns
- Creates visual, interactive architecture diagrams
The 3-Phase Analysis Process
Phase 1: File Identification & Technology Detection
What Happens:
- Recursive scan of entire repository
- Pattern-based identification of key files
- Technology stack detection (50+ languages, 100+ frameworks)
- Component categorization (frontend, backend, database, infrastructure)
- Dependency file analysis
Technologies Detected:
Languages Supported (50+):
- Backend: Python, Java, Go, Rust, C#, PHP, Ruby, Node.js/TypeScript
- Frontend: JavaScript, TypeScript, React, Vue, Angular, Svelte
- Mobile: Swift, Kotlin, Dart (Flutter), React Native
- Data: SQL, R, Scala, Elixir
- Systems: C, C++, Rust, Go, Zig
- Functional: Haskell, Erlang, F#, OCaml
Frameworks Detected (100+):
- Web: Django, Flask, FastAPI, Express, Spring Boot, ASP.NET, Rails
- Frontend: React, Vue, Angular, Next.js, Nuxt, Svelte, Solid
- Mobile: Flutter, React Native, SwiftUI, Jetpack Compose
- Data: Pandas, Spark, Hadoop, Airflow, Kafka
- ML/AI: TensorFlow, PyTorch, Scikit-learn, Keras
Infrastructure Tools:
- Containers: Docker, Kubernetes, Docker Compose, Helm
- CI/CD: GitHub Actions, GitLab CI, Jenkins, CircleCI
- IaC: Terraform, CloudFormation, Pulumi, Ansible
- Databases: PostgreSQL, MySQL, MongoDB, Redis, Elasticsearch
File Categories:
Dependency Files
├── requirements.txt, package.json, pom.xml, Cargo.toml
├── Pipfile, yarn.lock, go.mod, Gemfile
Configuration Files
├── .env, config.yaml, settings.py, application.properties
├── docker-compose.yml, nginx.conf, webpack.config.js
Container/Deployment
├── Dockerfile, Kubernetes manifests, Helm charts
├── docker-compose.yml, .gitlab-ci.yml, Jenkinsfile
Database Files
├── Schema definitions, migrations, seeds
├── SQL scripts, ORM models
Test Files
├── Unit tests, integration tests, E2E tests
├── Test configs, fixtures, mocks
Documentation
├── README, API docs, architecture diagrams
├── User guides, developer guides
Performance:
- Processes 10,000+ files in minutes
- Multi-threaded analysis (10 concurrent workers)
- Smart filtering excludes generated files, binaries
- Typical speed: 1,000-5,000 files/minute
Phase 1 Output Example:
Phase 1 Complete: 2,847 files analyzed, 12 categories identified
Technologies Found:
- Languages: Python, JavaScript, TypeScript, SQL
- Frameworks: Django, React, PostgreSQL, Redis
- Infrastructure: Docker, Kubernetes, Nginx, GitHub Actions
- Tools: pytest, Jest, Black, ESLint, Webpack
File Categories:
- Dependency Files: 8 files
- Configuration: 24 files
- Container/Deployment: 15 files
- Database: 47 files
- Test Files: 312 files
Phase 2: LLM-Powered Deep Dive & Classification
What Happens:
- AI agents analyze each component deeply
- Extract architectural intent and purpose
- Identify component roles and responsibilities
- Detect relationships and dependencies
- Assess architectural implications
AI Analysis Per Component:
For Dependency Files:
{ "file_type": "Python Requirements", "primary_language": "Python", "package_manager": "pip", "tech_stack": [ "Django 4.2", "PostgreSQL", "Redis", "Celery", "gunicorn" ], "frameworks": ["Django REST Framework", "Django Channels"], "databases": ["PostgreSQL", "Redis"], "infrastructure_tools": ["Docker", "Kubernetes"], "architectural_implications": "Monolithic backend with async task processing, WebSocket support for real-time features, stateless API design for horizontal scaling" }
For Configuration Files:
{ "config_type": "Application Configuration", "environment": "Production", "services_configured": [ "Database connection pooling", "Redis caching layer", "Email service (SendGrid)", "Object storage (AWS S3)" ], "security_settings": ["HTTPS enforced", "CORS configured", "Rate limiting enabled"], "architectural_role": "Central configuration for production deployment with external service integrations" }
For Container/Deployment:
{ "deployment_type": "Kubernetes Deployment", "container_strategy": "Multi-stage Docker build with Alpine Linux", "orchestration": "Kubernetes with Helm charts", "scaling_strategy": "Horizontal Pod Autoscaler based on CPU/memory", "networking": "Nginx ingress with SSL termination", "architectural_implications": "Cloud-native, microservices-ready, supports zero-downtime deployments" }
For Database Files:
{ "database_type": "PostgreSQL", "schema_complexity": "Moderate (47 tables, 12 relationships)", "migration_strategy": "Django ORM migrations with version control", "data_relationships": [ "User -> Orders (1:N)", "Products -> Categories (N:M)", "Audit -> Results (1:N with cascade)" ], "performance_considerations": "Indexes on foreign keys, materialized views for reporting" }
AI Analysis Capabilities:
Architecture Pattern Detection:
- Monolithic vs. Microservices
- Layered architecture (MVC, 3-tier, N-tier)
- Event-driven architecture
- CQRS (Command Query Responsibility Segregation)
- Hexagonal/Clean architecture
Technology Stack Assessment:
- Version compatibility analysis
- Framework ecosystem understanding
- Integration points identification
- Scalability implications
- Security posture evaluation
Component Relationship Detection:
- API endpoints and consumers
- Database connections
- Message queue flows
- Cache layers
- External service integrations
Rate Limiting & Performance:
- 200 LLM calls per minute (configurable)
- Automatic retry with exponential backoff
- Thread-safe global rate limiter
- Concurrent analysis (up to 10 workers)
- Typical time: 10-30 minutes for medium repos
Phase 2 Output Example:
Phase 2 Complete: 68 AI analyses performed
Component Analyses:
- Dependency Files: 8 analyzed
- Configuration: 24 analyzed
- Deployment: 15 analyzed
- Database: 21 analyzed
Architectural Insights Generated:
- Primary Architecture: Monolithic with microservice preparation
- Scaling Strategy: Horizontal scaling ready
- Data Layer: Relational DB with caching layer
- Integration Pattern: RESTful APIs with WebSocket support
Phase 3: Graph Synthesis & Relationship Mapping
What Happens:
- Combines Phase 1 & 2 results
- Constructs interactive architecture graph
- Maps relationships between components
- Organizes into logical layers
- Generates visual representation
Graph Structure:
Nodes (Components):
{ "id": "django-backend", "label": "Django Application", "type": "backend_service", "column": "code_related", "technologies": ["Python", "Django", "DRF"], "confidence": 0.95, "metadata": { "lines_of_code": 15420, "complexity": "Medium", "test_coverage": "78%" } }
Edges (Relationships):
{ "source": "django-backend", "target": "postgresql-db", "relationship": "reads_writes", "protocol": "SQL", "confidence": 0.98 }
Graph Columns (Layers):
┌─────────────────────┐
│ Code Related │ Frontend, Backend, Services
├─────────────────────┤
│ Database/ │ Databases, Message Queues,
│ Communication │ Cache, API Gateways
├─────────────────────┤
│ Server/ │ Containers, Orchestration,
│ Deployment │ CI/CD, Infrastructure
└─────────────────────┘
Example Architecture Graph:
[React Frontend] ──HTTP/REST──> [Nginx Reverse Proxy]
│
├──> [Django API Service]
│ │
│ ├──SQL──> [PostgreSQL]
│ │
│ ├──Cache──> [Redis]
│ │
│ └──Queue──> [Celery Workers]
│ │
│ └──> [Redis Queue]
│
└──WebSocket──> [Django Channels]
Infrastructure Layer:
[Docker Containers] deployed on [Kubernetes]
[GitHub Actions CI/CD] ──> [Docker Registry] ──> [K8s Cluster]
Phase 3 Output Example:
Phase 3 Complete: Graph constructed with 42 nodes and 87 edges
FINAL ARCHITECTURE SUMMARY:
Files Analyzed: 2,847
AI Analyses: 68
Graph Nodes: 42
Graph Edges: 87
Code-related: 15 components
Database/Comm: 18 components
Server/Deploy: 9 components
Architecture Type: Monolithic with microservice-ready design
Primary Technologies: Python/Django, React, PostgreSQL, Redis
Deployment: Dockerized, Kubernetes orchestration
Scaling Approach: Horizontal pod autoscaling
Example Architecture Analysis Output
🏗️ Example: Technology Stack & Architecture Assessment
Technology Stack Detected
Programming Languages:
- Python 3.11 (15,420 LOC) - Backend
- TypeScript (8,932 LOC) - Frontend
- SQL (1,247 LOC) - Database
Backend Frameworks:
- Django 4.2.8 🟢 (Latest stable)
- Django REST Framework 3.14.0 🟢
- Celery 5.3.4 🟢 (Async task processing)
- Django Channels 4.0.0 🟢 (WebSocket support)
Frontend Frameworks:
- React 18.2.0 🟢 (Latest)
- Redux Toolkit 2.0.1 🟢
- PrimeReact 10.2.0 🟢
- Axios 1.6.2 🟢
Databases & Caching:
- PostgreSQL 15 🟢 (Primary database)
- Redis 7.2 🟢 (Cache & message broker)
Infrastructure:
- Docker 24.0 🟢 (Containerization)
- Kubernetes 1.28 🟢 (Orchestration)
- Nginx 1.25 🟢 (Reverse proxy)
- GitHub Actions 🟢 (CI/CD)
External Services:
- AWS S3 (Object storage)
- SendGrid (Email delivery)
- Stripe (Payment processing)
- Sentry (Error tracking)
Architecture Diagram (Text Representation)
┌─────────────────────────────────────────────────────────────────┐
│ CODE RELATED LAYER │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌───────────────┐ ┌──────────────────┐ │
│ │ React Frontend │──HTTP/REST──→│ Nginx Load │ │
│ │ │ │ Balancer │ │
│ │ • TypeScript │ │ │ │
│ │ • Redux │ │ • SSL Termination│ │
│ │ • PrimeReact │ │ • Rate Limiting │ │
│ └───────────────┘ └────────┬─────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────────┐ │
│ │ Django API │ │
│ │ Application │ │
│ │ │ │
│ │ • REST API │ │
│ │ • Authentication │ │
│ │ • Business Logic │ │
│ └────────┬─────────┘ │
│ │ │
│ ├──────────────┐ │
│ │ │ │
└───────────────────────────────────────────┼──────────────┼───────┘
▼ ▼
┌─────────────────────────────────────────────────────────────────┐
│ DATABASE & COMMUNICATION LAYER │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────┐ ┌──────────────┐ │
│ │ PostgreSQL │ │ Redis │ │
│ │ Database │ │ │ │
│ │ │ │ • Cache │ │
│ │ • User Data │ │ • Sessions │ │
│ │ • Audit Data │ │ • Task Queue │ │
│ │ • Transactions│ └──────┬───────┘ │
│ └──────────────┘ │ │
│ ▼ │
│ ┌──────────────┐ │
│ │ Celery │ │
│ │ Workers │ │
│ │ │ │
│ │ • Async Tasks│ │
│ │ • Email Jobs │ │
│ │ • Reports │ │
│ └──────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
┌─────────────────────────────────────────────────────────────────┐
│ SERVER & DEPLOYMENT LAYER │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌─────────────────────────────────────────────────┐ │
│ │ Kubernetes Cluster (AWS EKS) │ │
│ │ │ │
│ │ ┌───────────┐ ┌───────────┐ ┌─────────────┐ │ │
│ │ │Django Pod │ │Celery Pod │ │ Redis Pod │ │ │
│ │ │ (×3 replicas)│ │ (×2 workers)│ │ (×1 primary)│ │ │
│ │ └───────────┘ └───────────┘ └─────────────┘ │ │
│ │ │ │
│ │ Auto-scaling: CPU > 70% → Add pods │ │
│ │ Health Checks: Every 10s │ │
│ └─────────────────────────────────────────────────┘ │
│ │
│ ┌─────────────────────────────────────────────────┐ │
│ │ GitHub Actions CI/CD Pipeline │ │
│ │ │ │
│ │ Push → Test → Build → Docker Registry → Deploy │ │
│ │ │ │
│ │ • Unit Tests (pytest, Jest) │ │
│ │ • Linting (Black, ESLint) │ │
│ │ • Security Scan (Trivy) │ │
│ │ • Zero-downtime deployment │ │
│ └─────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
Architectural Assessment
Architecture Pattern: 🟡 Monolithic with Microservices Preparation
- Current state: Single Django application
- Well-separated domains within monolith
- Clean API boundaries enable future extraction
- Stateless design supports horizontal scaling
Scalability Analysis:
| Aspect | Status | Details |
|---|---|---|
| Horizontal Scaling | 🟢 Ready | Stateless pods, load balanced |
| Database Scaling | 🟡 Bottleneck | Single primary, needs read replicas |
| Caching Strategy | 🟢 Implemented | Redis caching, 89% hit rate |
| Async Processing | 🟢 Implemented | Celery for background jobs |
| Static Assets | 🟢 Optimized | CDN delivery via CloudFront |
| Session Management | 🟢 Stateless | Redis-backed sessions |
Risk Assessment:
🔴 Critical Risks:
- Single database primary (no read replicas)
- Impact: Limited read scaling
- Mitigation: Add 2 read replicas ($150/month)
🟡 Medium Risks:
-
Monolithic structure limits team parallelization
- Impact: Deployment coordination overhead
- Mitigation: Extract authentication service first
-
Redis single instance (no clustering)
- Impact: Cache unavailable during failures
- Mitigation: Redis Cluster ($200/month)
🟢 Strengths:
- Modern tech stack (all dependencies up-to-date)
- Container-native design
- Comprehensive test coverage (78%)
- Proper separation of concerns
Technology Debt Assessment: 🟢 Low
- All frameworks on supported versions
- No deprecated dependencies detected
- Security patches current
- Migration path clear for future upgrades
Microservices Extraction Roadmap
Phase 1: Authentication Service (Q1)
Components to Extract:
- User authentication logic
- JWT token management
- Session handling
Benefits:
- Security isolation
- Independent scaling (high read volume)
- Team can work independently
Estimated Effort: 4 weeks, 2 engineers
ROI: Enables parallel development, reduces blast radius
Phase 2: Payment Processing (Q2)
Components to Extract:
- Stripe integration
- Payment webhooks
- Transaction history
Benefits:
- PCI compliance simplification
- Security isolation for financial data
- Independent deployment cycle
Estimated Effort: 6 weeks, 3 engineers
ROI: Reduced compliance scope, better security posture
Phase 3: Notification Service (Q3)
Components to Extract:
- Email delivery
- SMS notifications
- WebSocket events
Benefits:
- Scale independently (burst traffic)
- Resilience (notifications don't block main app)
- Multi-tenancy ready
Estimated Effort: 4 weeks, 2 engineers
ROI: Improved reliability, better user experience
Confidence Scores:
- Technology Detection: 98% (All major frameworks identified)
- Relationship Mapping: 92% (Some dynamic relationships inferred)
- Architecture Pattern: 95% (Clear monolithic structure)
- Scaling Assessment: 90% (Based on static analysis + best practices)
What You Get
Interactive Architecture Diagram
Visual Components:
- Color-coded nodes by type (frontend, backend, database, infra)
- Directional edges showing data flow
- Hover details for each component
- Click to see code files associated with component
- Zoom, pan, and filter capabilities
Comprehensive Technology Report
Technology Inventory:
## Technology Stack Summary ### Programming Languages - Python 3.11 (Backend, 15,420 LOC) - TypeScript/JavaScript (Frontend, 8,932 LOC) - SQL (Database, 1,247 LOC) ### Frameworks & Libraries - Django 4.2 (Web framework) - React 18 (Frontend framework) - Django REST Framework (API layer) - Celery (Task queue) ### Databases & Storage - PostgreSQL 15 (Primary database) - Redis 7 (Cache & message broker) ### Infrastructure - Docker (Containerization) - Kubernetes (Orchestration) - Nginx (Reverse proxy) - GitHub Actions (CI/CD) ### External Integrations - AWS S3 (Object storage) - SendGrid (Email service) - Stripe (Payments)
Architectural Assessment
Architecture Pattern:
- Primary: Monolithic application
- Evolution Path: Microservices-ready
- Communication: RESTful APIs + WebSockets
- Data Layer: RDBMS with caching
Scalability Analysis:
- Horizontal Scaling: ✅ Supported (stateless design)
- Vertical Scaling: ✅ Database can scale vertically
- Load Balancing: ✅ Nginx configured
- Caching Strategy: ✅ Redis caching implemented
- Async Processing: ✅ Celery task queue
Risk Assessment:
- Single Point of Failure: Database (recommend read replicas)
- Tight Coupling: Monolithic structure (migration path exists)
- Technology Debt: Minimal (modern versions)
- Scalability Bottleneck: Database writes (recommend sharding strategy)
Use Cases
For Investors (Due Diligence)
Quick Assessment:
- Is the architecture modern and scalable?
- Are they using outdated technologies?
- What would it cost to scale 10x?
- Are there single points of failure?
Example Insight:
"Architecture is microservices-ready but currently monolithic. Clean separation of concerns allows incremental migration. Database is the scaling bottleneck—budget $50k for read replicas and $150k for sharding if growth exceeds 1M users."
For CTOs (Technical Planning)
Refactoring Roadmap:
- Which services to extract first?
- What's the critical path for scalability?
- Where is technical debt concentrated?
- What infrastructure upgrades are needed?
Example Roadmap:
Phase 1 (Q1): Extract authentication service
- Already well-isolated in codebase
- High read volume, good microservice candidate
- Effort: 4 weeks, 2 engineers
Phase 2 (Q2): Implement database read replicas
- Current bottleneck for scaling
- Reduces primary DB load by 70%
- Effort: 2 weeks, 1 engineer
Phase 3 (Q3): Extract payment processing service
- Security isolation benefits
- PCI compliance simplification
- Effort: 6 weeks, 3 engineers
For M&A Advisors
Integration Assessment:
- How does this stack integrate with acquirer's tech?
- What's the replatforming cost?
- Are there incompatible technologies?
- What's the knowledge transfer complexity?
Key Advantages
vs. Manual Architecture Review
| Manual Review | CodeDD Architecture Analysis |
|---|---|
| Takes days/weeks | Takes minutes/hours |
| Human interpretation bias | AI-powered objective analysis |
| May miss hidden components | Scans entire codebase |
| Static, outdated quickly | Generated from actual code |
| Expensive ($10k-$50k) | Included in audit |
vs. Traditional Dependency Tools
| Traditional Tools | CodeDD |
|---|---|
| Shows only code dependencies | Full architecture mapping |
| No architectural context | AI understands architectural patterns |
| Requires configuration | Automatic detection |
| Limited to one language/framework | 50+ languages, 100+ frameworks |
| No visual representation | Interactive visual graph |
Technical Details
Supported Ecosystems
Complete coverage for:
- Python ecosystem (Django, Flask, FastAPI, + 30 frameworks)
- JavaScript/Node.js (React, Vue, Angular, Express, + 40 frameworks)
- Java (Spring, Quarkus, Micronaut, + 25 frameworks)
- .NET (ASP.NET, Blazor, + 15 frameworks)
- Go (Gin, Echo, Fiber, + 10 frameworks)
- Ruby (Rails, Sinatra, + 8 frameworks)
Partial coverage for:
- PHP, Rust, Kotlin, Swift, Scala, Elixir
- Emerging languages and frameworks
Processing Performance
Typical Performance:
- Phase 1: 1-5 minutes (file scanning)
- Phase 2: 10-30 minutes (AI analysis)
- Phase 3: 1-3 minutes (graph synthesis)
- Total: 15-40 minutes for medium repository
Factors Affecting Time:
- Number of unique technologies (more = slower)
- Number of configuration files (more AI calls needed)
- Repository size
- LLM API response times
Accuracy & Confidence
High Confidence (>90%):
- Well-documented frameworks with clear patterns
- Standard project structures
- Popular technologies with extensive training data
Medium Confidence (70-90%):
- Custom frameworks or unusual patterns
- Mixed technology stacks
- Non-standard project structures
Low Confidence (<70%):
- Highly customized or proprietary systems
- Undocumented internal frameworks
- Legacy systems with uncommon patterns
AI includes confidence scores with every finding to help you assess reliability.
Limitations
What Architecture Analysis Can't Do
Not Included:
- Runtime behavior analysis (requires live system)
- Performance profiling (static analysis only)
- Actual data flow tracing (inferred, not measured)
- Security vulnerability scanning (separate feature)
Accuracy Limitations:
- Custom internal frameworks may not be recognized
- Dynamically loaded components may be missed
- Microservices across multiple repos need separate scans
- Architecture assumes code reflects reality (may have dead code)
Key Takeaways
For Investors:
- Instant Architecture Understanding: No need to interview developers
- Scalability Assessment: Know if it can handle growth
- Technology Risk: Identify outdated or problematic tech choices
- Integration Cost: Estimate re-platforming or integration costs
- Comparative Analysis: Benchmark against industry standards
For CTOs:
- Onboarding Acceleration: New team members understand system quickly
- Refactoring Planning: Data-driven microservices extraction roadmap
- Technology Audit: Comprehensive inventory of everything in use
- Documentation: Automatically maintained architecture diagrams
- Technical Debt: Visualize where complexity and coupling exist
Next Steps
- Learn about Cross-File Contextualization
- Understand Audit Consolidation & Risk Scoring
- Review AI-Powered File Analysis