Architecture Analysis & Mapping
How CodeDD maps your software architecture from actual code
On this page
Most codebases lack up-to-date architecture documentation. CodeDD reverse-engineers structure from the code itself — producing an interactive map of components, technologies, relationships, and data flows.
What you get#
Interactive architecture diagram — color-coded nodes (frontend, backend, database, infrastructure) with directional edges showing data flow. Click a component to see associated files. Zoom, pan, and filter in the dashboard.
Technology inventory — languages, frameworks, databases, external services, and infrastructure tools detected across the repository, with version information where available.
Architectural assessment — detected patterns (monolith, layered, microservices-ready), scalability indicators, coupling analysis, and risk highlights (single points of failure, outdated stack, missing abstractions).
Test coverage by component — which parts of the system are well-tested and which are not.
How it works (at a high level)#
CodeDD combines three approaches:
- Pattern detection — scans manifests, configs, and source structure to identify technologies, dependency files, deployment configs, and database schemas across major programming languages and frameworks.
- AI classification — analyzes key components to determine role, tech stack, and architectural implications.
- Graph synthesis — builds the interactive diagram with components organized into layers (code, data/communication, deployment) and mapped relationships.
Confidence scores accompany findings so you can assess reliability — standard stacks score higher; heavily customized systems may have lower confidence on inferred relationships.
Typical domains detected#
- Application services (APIs, business logic, workers)
- Frontend clients (React, Vue, Angular, mobile)
- Data stores (PostgreSQL, MySQL, MongoDB, Redis, message queues)
- Infrastructure (Docker, Kubernetes, CI/CD, reverse proxies)
- External integrations (payment, email, storage, monitoring)
Use cases#
Investors — Is the architecture modern? Are there scaling bottlenecks or single points of failure? What would integration or replatforming cost?
CTOs — Onboarding map for new engineers. Data-driven refactoring priorities. Technology audit inventory.
M&A advisors — Stack compatibility with acquirer tech, knowledge transfer complexity, replatforming scope.
Limitations#
Architecture analysis is static — it reads code, not runtime behavior.
- Data flows are inferred, not measured live
- Dynamically loaded components may be missed
- Microservices split across multiple repos need separate audits per repo
- Custom internal frameworks may not be fully recognized
Security vulnerability scanning is a separate feature covered in AI-Powered File Analysis.