Architecture Analysis & Mapping

How CodeDD maps your software architecture from actual code

Architecture Analysis & Mapping

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microservices-ready), scalability indicators, coupling analysis, and risk highlights (single points of failure, outdated stack, missing abstractions).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 50+ languages.
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.