---
title: Architecture Analysis & Mapping
description: How CodeDD maps your software architecture from actual code
category: Software Audit
order: 5
---

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microservices-ready), scalability indicators, coupling analysis, and risk highlights (single points of failure, outdated stack, missing abstractions).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

1. **Pattern detection** — scans manifests, configs, and source structure to identify technologies, dependency files, deployment configs, and database schemas across major programming languages and frameworks.
2. **AI classification** — analyzes key components to determine role, tech stack, and architectural implications.
3. **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](/documentation/ai-powered-file-analysis).

## Next steps

- [Cross-File Contextualization](/documentation/cross-file-contextualization)
- [Audit Consolidation & Risk Scoring](/documentation/audit-consolidation-risk-scoring)
- [AI-Powered File Analysis](/documentation/ai-powered-file-analysis)