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Fahrsimulator_MSY2526_UX/docs/PROJECT_REVIEW.md

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Project Review

Purpose

The virtual health assistant is built as an event-driven desktop system that converts binary health predictions into a continuously updated risk visualization.

Primary goals:

  1. receive predictions in near real time via MQTT,
  2. persist events locally,
  3. compute rolling risk levels,
  4. provide immediate visual feedback in the UI,
  5. export machine-readable state for external avatar integration.

End-to-End Pipeline

  1. MqttClientService subscribes to the configured topic.
  2. Payloads are forwarded to BinaryEventService.
  3. Payloads are validated (valid, _id, prediction).
  4. Accepted predictions are stored by DataPersistenceService.
  5. StatisticsService computes the current ratio from the latest 20 entries.
  6. EvaluationService maps ratio to ProblemLevel.
  7. AppState notifies UI controllers and views.
  8. AnimationFileService writes animation.json.

Functional Scope

  • Desktop monitoring UI with two views (stream + dashboard)
  • MQTT-driven event ingestion
  • SQLite event persistence
  • Rolling ratio analytics
  • Level classification (NONE, WARNING, HIGH, DISASTER)
  • Optional external process orchestration (simulator + Unreal launcher)
  • Startup configuration validation and structured logging

Design Highlights

  • Separation of concerns
    Startup/shutdown orchestration lives in bootstrap; business logic in service; view orchestration in controller.
  • Config-first runtime behavior
    MQTT, simulator, Unreal, and animation output are controlled via properties.
  • Fail-fast startup validation
    AppConfigValidator blocks startup when enabled integrations are misconfigured.
  • Testability by construction
    Multiple services support dependency injection in tests (for example process launchers and MQTT client instances).

Quality and Verification

Automated tests cover:

  • App state transitions and listener notifications
  • Payload validation and duplicate-id handling
  • Ratio and threshold evaluation behavior
  • SQLite persistence integration behavior
  • MQTT subscribe/publish/callback flow
  • Process startup/shutdown command sequencing and startup report behavior
  • Animation file mapping and overwrite behavior

See Testing for details and command examples.

Current Limitations

  • Runtime database is deleted on shutdown (ApplicationShutdownManager), so historical data is not retained.
  • Default Unreal-related paths are machine-specific and must be adapted for new environments.
  • UI behavior is not covered by end-to-end GUI tests.
  • Duplicate payload protection is based on the last seen _id only (consecutive duplicates).

Improvement Roadmap

  1. Make database cleanup optional through configuration.
  2. Add profile-based configuration (local/dev/demo) to reduce machine-specific defaults.
  3. Add UI-level automated checks (or contract tests around controller/view boundaries).
  4. Improve duplicate handling (e.g., bounded cache of recent IDs).
  5. Add metrics/telemetry for startup health and message throughput.