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Test data types

Positive Test Data

Positive test data includes inputs and conditions designed to yield a successful, expected outcome. This type of data confirms that the system operates as intended with valid inputs, ensuring it performs its primary functions correctly. To achieve thorough testing, generating synthetic test data can offer a variety of scenarios.

Negative Test Data

Negative and valid test data aims to provoke error conditions or exceptions within the software. It assesses the system's response to unexpected inputs and error scenarios, ensuring effective error handling and smooth recovery. Using synthetic test data can enhance this process by covering a broader range of error conditions.

Boundary Test Data

  • Boundary values include values at the edge of valid and invalid partitions. This method focuses on testing the extreme ends of input values, such as minimum and maximum values, rather than just the middle. For example, if the quantity of the product can only have a range from 1 to 10, the boundary values would be 1, 10, and values just above and below these boundaries (e.g., 0 and 11).

  • Equivalence Partitioning divides input data into partitions that are treated equally during testing. Boundary testing then focuses on the edges of these partitions. This is useful when the requirement allows for range values such as scores or conditions that when a field reaches a certain threshold.

  • Single Fault Assumption works when testing multiple variables, this approach assumes that only one variable will be at its boundary value while others remain at nominal values.

Invalid Test Data

Test data that is normally associated with negative test data but covers more on how the system handles unexpected inputs to the system.

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