# Testing Strategy: Template Mapping Engine

This document outlines the testing strategy for the Template Mapping Engine to ensure production readiness.

## 1. Backend Testing (Python/FastAPI)

### Unit Testing
*   **Framework**: `pytest`
*   **Database**: Use an in-memory SQLite database (`sqlite:///:memory:`) or a dedicated test PostgreSQL container via `testcontainers`.
*   **Mocks**: 
    *   Mock OCR engines (`pytesseract`, `pdfplumber`) to avoid slow I/O during test suites.
    *   Mock file uploads using `fastapi.testclient.TestClient`.
*   **Coverage Targets**:
    *   **Engines**: 100% logic coverage for `TemplateRecognitionEngine` and scoring weights.
    *   **Services**: 90% coverage for CRUD operations.

### Integration Testing
*   Test end-to-end API flows:
    1.  `POST /api/documents/upload` with a sample PDF.
    2.  Wait for layout extraction.
    3.  `POST /api/templates` to create a template.
    4.  `POST /api/templates/{id}/mappings/save` to map extracted data.
    5.  `POST /api/documents/upload` with a similar document to verify `POST /api/documents/{id}/recognize` returns the correct template match.

## 2. Frontend Testing (Angular)

### Unit Testing
*   **Framework**: Jasmine & Karma (or Jest if configured).
*   **Component Tests**:
    *   Verify `TemplatesComponent` renders the left and right panels.
    *   Verify the `AddField` dialog toggles correctly and validates empty inputs.
*   **Service Tests**:
    *   Mock `HttpClient` using `HttpTestingController` to ensure `TemplateService` sends correct payloads to the backend.

### E2E / Integration Testing
*   **Framework**: Cypress or Playwright.
*   **Critical User Journeys (CUJ)**:
    1.  User uploads a document, UI displays the layout preview visually.
    2.  User creates a template and adds 3 fields.
    3.  User drags a block from the Document Preview and drops it into a Template Field.
    4.  User saves the mapping successfully.

## 3. OCR & Layout Extraction Accuracy Testing

Since the OCR engine relies on visual heuristics rather than AI models, testing its accuracy is crucial to prevent regressions.

*   **Golden Dataset**: Create a dataset of 50-100 real-world business documents (Invoices, POs, Receipts).
*   **Evaluation Metric**: Run the `DocumentProcessor` over the Golden Dataset and compare the output bounding boxes and classifications (HEADER, VENDOR, etc.) against manually annotated Ground Truth data.
*   **Acceptance Criteria**: Maintain > 92% classification accuracy for logical block types.

## 4. Performance & Load Testing

*   **Tool**: `locust` or `k6`.
*   **Scenario**: Simulate 50 concurrent users uploading 2MB PDF documents simultaneously to ensure the `DocumentProcessor` does not exhaust server memory (OpenCV and Tesseract can be memory-intensive).
*   **Optimization**: Ensure the `process_file` logic can be offloaded to Celery workers if the API starts blocking or timing out under load.