Data-Driven Testing is a scripting technique that utilises external data files to contain test data and expected results needed for test script execution. Key features include:
Purpose:
- Separate test logic from test data to enhance reusability.
- Allow easy modification of test scenarios without changing code.
Implementation:
- Test scripts read input from external sources like CSV files or databases.
- Each iteration uses different sets of input data to execute the same test case.
Advantages:
- Reduces redundancy in test scripts.
- Facilitates extensive testing with varied datasets.
Applications:
- Useful in functional testing where multiple input combinations need validation.
- Commonly used in automated testing frameworks.
Challenges:
- Requires proper management of external data sources.
- Complexity increases with larger datasets or more intricate test scenarios.
Data-Driven Testing enhances flexibility and efficiency in test automation strategies.