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Architecture overview

Aucert uses a 5-layer AI pipeline to automatically generate, execute, and analyze mobile application tests. Each layer is specialized for a distinct phase of the quality engineering process.

The 5 layers​

L1: Generation​

The Generation layer designs test scenarios using context from the Knowledge Graph — a structured representation of your application's screens, navigation flows, API contracts, and historical bugs.

Rather than writing test scripts manually, the AI generates comprehensive test plans that cover user flows, edge cases, and regression scenarios.

L2: Execution​

The Execution layer runs generated tests on Android emulators. It navigates your app, performs actions (taps, swipes, text input), and captures screenshots at each step.

L3: Analysis​

The Analysis layer applies visual reasoning to screenshots and execution logs. It compares expected behavior against actual results, identifying UI regressions, functional failures, and unexpected states.

L4: Decision​

The Decision layer determines pass/fail status using confidence scoring. Tests that score above the configured threshold pass automatically. Ambiguous results escalate through a multi-stage verification process.

L5: Reporting​

The Reporting layer generates structured bug reports with reproduction steps, severity classification, screenshots, and confidence scores. Reports integrate directly with your CI/CD pipeline and issue tracker.

Cross-cutting components​

Knowledge Graph​

The Knowledge Graph feeds context into the Generation layer. It ingests:

  • Application code structure
  • UI screen maps
  • API schemas
  • Historical test results and bug reports

Data flow​

Client → API (v1) → Pipeline Orchestrator → [L1→L2→L3→L4→L5] → Response

All inter-layer communication uses a structured message envelope (MCP) carrying task IDs, payloads, confidence scores, and trace IDs.

What's next​