Knowledge Graph
The Knowledge Graph (KG) is Aucert's structured representation of your application. It captures the relationships between screens, user flows, API endpoints, and data models to power intelligent test generation.
What the Knowledge Graph captures
| Source | What it extracts |
|---|---|
| Application code | Screen definitions, navigation paths, state management |
| API schemas | Endpoints, request/response shapes, authentication flows |
| UI layouts | Screen hierarchy, interactive elements, accessibility labels |
| Historical data | Past test results, known bug patterns, regression areas |
How it works
The KG represents your app as a graph of nodes (screens, components, API endpoints) and edges (navigation transitions, data flows, dependencies). This structure enables the Generation layer to:
- Identify critical paths — User flows that traverse multiple screens
- Detect edge cases — Unusual state combinations from node properties
- Prioritize testing — Focus on high-impact areas based on code change frequency
Data model
The Knowledge Graph uses nodes and edges:
- Nodes — Entities with an ID, type (screen, component, endpoint), name, and extensible properties
- Edges — Relationships between nodes (navigates_to, calls, depends_on)
info
The Knowledge Graph is stored in PostgreSQL with JSONB for flexible schema evolution. This provides the query flexibility of a graph database without the operational overhead.
What's next
- Architecture overview — Full pipeline architecture
- Device Twin — How Aucert handles emulator behavior