Testing and Validation of Real-Time and Historical Data Functionality
User Story
As a QA engineer, I want to thoroughly test and validate the real-time and historical data retrieval functionality so that I can ensure the data is accurate and the system performs as expected.
Description
This use case involves performing comprehensive testing of the system’s real-time and historical data functionality. The QA team will validate the entire flow of data, from sensor data being collected and stored in the database, to it being displayed on the dashboard. Tests will cover data retrieval, pagination, export functionality (CSV/JSON), and proper error handling. Performance testing will also be conducted to ensure the system handles large datasets efficiently.
Acceptance Criteria
[ ] Real-time data is accurately displayed on the dashboard and refreshes at the expected interval (e.g., every 10 seconds).
[ ] Historical data can be filtered by date range and is displayed correctly in both table and graph formats.
[ ] Data export (CSV and JSON) works as expected and outputs the correct data.
[ ] Error messages are correctly shown when data cannot be retrieved or is missing.
[ ] The system performs well, even with large datasets, without significant performance degradation.
[ ] Test cases for all major flows (real-time data, historical data, export, error handling) are written and executed.
Testing
Normal Flow of Events
The QA team validates that real-time sensor data is correctly displayed on the dashboard and refreshes automatically at the specified interval.
The QA team filters historical data by various date ranges and verifies that the data is displayed in table and graph formats.
The QA team tests the export functionality (CSV and JSON) and ensures the correct data is exported.
The QA team checks the system’s performance when handling large datasets and ensures that it meets performance benchmarks.
Alternate/Exceptional Flows:
S-1:Data retrieval issues
The system fails to retrieve data, and the error message “Unable to retrieve data” is displayed correctly.
The QA team ensures that appropriate error handling is triggered
S-2: Incorrect or missing data in export files
The exported CSV or JSON file contains incorrect or incomplete data.
The QA team documents the issue and ensures the export functionality is fixed.
S-3: System slows down with large datasets
The system experiences performance issues while retrieving or displaying large historical datasets.
The QA team reports performance issues and works with the development team to optimize queries and pagination.
Testing and Validation of Real-Time and Historical Data Functionality
User Story
As a QA engineer, I want to thoroughly test and validate the real-time and historical data retrieval functionality so that I can ensure the data is accurate and the system performs as expected.
Description
This use case involves performing comprehensive testing of the system’s real-time and historical data functionality. The QA team will validate the entire flow of data, from sensor data being collected and stored in the database, to it being displayed on the dashboard. Tests will cover data retrieval, pagination, export functionality (CSV/JSON), and proper error handling. Performance testing will also be conducted to ensure the system handles large datasets efficiently.
Acceptance Criteria
Testing
Normal Flow of Events
The QA team validates that real-time sensor data is correctly displayed on the dashboard and refreshes automatically at the specified interval. The QA team filters historical data by various date ranges and verifies that the data is displayed in table and graph formats. The QA team tests the export functionality (CSV and JSON) and ensures the correct data is exported. The QA team checks the system’s performance when handling large datasets and ensures that it meets performance benchmarks.
Alternate/Exceptional Flows:
S-1:Data retrieval issues
The system fails to retrieve data, and the error message “Unable to retrieve data” is displayed correctly. The QA team ensures that appropriate error handling is triggered
S-2: Incorrect or missing data in export files
The exported CSV or JSON file contains incorrect or incomplete data. The QA team documents the issue and ensures the export functionality is fixed.
S-3: System slows down with large datasets
The system experiences performance issues while retrieving or displaying large historical datasets. The QA team reports performance issues and works with the development team to optimize queries and pagination.