Equivalence Partitioning is a black-box testing technique used to divide the input domain of a system into classes or groups of data that are expected to exhibit similar behaviour. The goal of equivalence partitioning is to reduce the number of test cases while still ensuring adequate test coverage.
The basic idea behind equivalence partitioning is that if one particular input value in a partition behaves a certain way, then all other values in that partition should behave the same way. By selecting representative values from each partition, you can effectively test the behaviour of the entire partition.
When to apply Equivalence Partitioning
Equivalence Partitioning is useful in the following scenarios:
Input Validation: When testing input fields or parameters, such as user input or API inputs, you can identify representative values that belong to the same equivalence partition. Testing one value from each partition helps uncover defects related to input validation.
Test Case Optimization: Equivalence Partitioning allows you to reduce the number of test cases while still providing adequate coverage. Instead of testing every individual input value, you can select representative values from each partition, effectively covering a wide range of possible inputs.
Boundary Testing: Equivalence Partitioning is often combined with Boundary Value Analysis. By selecting values on the boundaries of each partition, you can test how the system behaves at the edges of the input domain. This helps in identifying issues related to boundary conditions.
Error Handling: Equivalence Partitioning can be applied to test the system’s response to different types of errors or exceptions. By selecting values that fall into error-prone partitions, you can verify if the system handles errors appropriately.
By applying Equivalence Partitioning, you can ensure that your testing effort is focused on representative values that have a higher probability of revealing defects. It helps in maximizing test coverage while minimizing the number of test cases needed, resulting in efficient and effective testing.