Testing Strategy for Helpline & AI Service

This strategy is modeled after the Testing Pyramid, emphasizing a strong foundation of unit tests, supported by integration and end-to-end tests, ensuring the quality, reliability, and performance of helplinev1 and ai_service.

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1. Static Analysis & Code Quality (The Foundation)

Objective: Enforce code style, catch syntax errors, and identify potential security vulnerabilities.

• ai_service (Python)
  • Linting: Ruff or Flake8 for PEP 8 compliance and logic errors.
  • Type Checking: MyPy to enforce static type hints.
  • Security: Bandit to scan for common vulnerabilities. Integration: Pre-commit hooks and mandatory CI pipeline step.

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2. Unit Testing

Objective: Verify individual components work correctly in isolation.

• ai_service

  • Framework: Pytest.
  • Models: Test model loading, preprocessing, prediction I/O contracts.
  • API Routes: Test endpoint logic, request validation, error handling (mock service layer).
  • Services: Test business logic, mocking external dependencies.
  • Coverage: Use pytest-cov, target >85%.

• helplinev1

  • Isolate core logic; mock ai_service calls during unit tests.

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3. Integration Testing

Objective: Ensure different components or services work together as expected.

• Intra-Service (ai_service):

  • Test flow from API endpoint → service layer → (mocked) model.
  • Use Docker to run tests against service in container.

• Inter-Service (helplinev1 ↔ ai_service):

  • Verify communication and correct handling of responses.
  • Use docker-compose to spin up both services in isolated network.

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4. Contract Testing

Objective: Prevent breaking changes between services.

• Method:
  1. helplinev1 generates a contract file (requests & expected responses).
  2. Share with ai_service.
  3. ai_service CI verifies API adheres to contract.
• Tool: Pact.

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5. End-to-End (E2E) Testing

Objective: Validate the entire workflow from start to finish.

• Scenarios:
  • User call initiated in helplinev1 → audio streamed to ai_service → transcription → summarization → final summary stored and displayed correctly.
  • User queries in helplinev1 retrieve data processed by ai_service.
• Tools: Pytest + requests, Playwright, or Cypress (if UI).

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6. Performance & Security Testing

• Performance Testing:

  • Objective: Ensure ai_service handles expected load within acceptable limits.
  • Tools: Locust, k6.
  • Types: Load, Stress, Soak Testing.

• Security Testing:

  • Objective: Identify and mitigate vulnerabilities.
  • Tools & Techniques:
    • Dependency Scanning: pip-audit, Snyk, Dependabot.
    • DAST: OWASP ZAP.

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CI/CD Pipeline Integration

Stage	Testing Activity	Purpose	Tools
Pre-Commit	Static Analysis	Catch issues before commit	pre-commit, ruff, mypy, bandit
On PR/Push	Unit & Integration Tests	Core logic verification	pytest, pytest-cov, Docker
On PR/Push	Contract Tests	Prevent API breaking changes	Pact
Post-Deploy (Staging)	E2E Tests	Full system workflow validation	pytest/requests, Playwright
Scheduled/Manual	Performance Tests	Ensure scalability & stability	Locust, k6
Scheduled/Manual	Security Scans	Find vulnerabilities	pip-audit, OWASP ZAP