Data Strategy

Test Data Strategy

Data Sources

The system utilizes a combination of open datasets and synthetic data to support reliable and realistic testing scenarios:

• Gathering Areas:
  Geographic data such as gathering areas are obtained from open data sources, specifically OpenStreetMap via Overpass API. However, to ensure deterministic and repeatable test results, automated tests do not rely on live API calls. Instead, they use fixed fixtures, stubbed HTTP responses, or frozen snapshots of previously retrieved data. Live provider checks are minimized and performed manually when necessary.

• Location Hierarchy (Country / City / District / Neighborhood):
  A predefined and version-controlled reference dataset is bundled within the repository in JSON format. This dataset represents the hierarchical structure of locations and is used to validate consistency, structural correctness, and completeness against the system’s API contract.

• Product Domain Data (Users, Profiles, Help Requests, Volunteers, Notifications, etc.):
  All domain-specific data is synthetically generated using data factories and seed scripts. The database is populated after a clean reset with realistic but non-sensitive values, such as disposable email formats and placeholder phone numbers, to simulate real-world usage without exposing personal information.

• News / Announcements:
  News and announcements are created internally by admin users within the system. For testing purposes, synthetic content (e.g., sample headlines and body text) is generated and associated with seeded admin accounts. No external or third-party news datasets are used.

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Data Accumulation Strategy

Test data is managed in a controlled and reproducible manner:

• The database is reset before each integration testing cycle to ensure a clean and consistent starting point.
• After resetting, only the minimum required data is seeded based on the specific test scenario.
• External geospatial API calls are replaced with predefined and controlled responses during automated testing (especially in CI environments) to eliminate dependency on external services and network variability.

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Data Validation Strategy

To ensure that the test data supports meaningful and realistic testing, the following validation approaches are applied:

• Assertions are performed on multiple levels, including:

  • HTTP response status codes
  • Response payload structures
  • Database state
  • Sorting behavior
  • Distance calculations
  • Authorization boundaries

• Scenario-based testing is used, where predefined user journeys (e.g., creating a help request, assigning volunteers, viewing nearby requests) are executed using known coordinates and controlled datasets.

• The data is designed to reflect realistic usage patterns while avoiding real personal data and live external dependencies, ensuring both privacy and test stability.