The input domain for testing is too large to test every input. So we can divide or partition the input domain based on a common feature or a class of data. Equivalence partitioning is a method for deriving test cases wherein classes of input conditions called equivalence classes are identified such that each member of the class causes the same kind of processing and output to occur. Thus, instead of testing every input, only one test case from each partitioned class can be executed. It means only one test case in the equivalence class will be sufficient to find errors. This test case will have a representative value of a class which is equivalent to a test case containing any other value in the same class. If one test case in an equivalence class detects a bug, all other test cases in that class have the same probability one of finding bugs. Therefore, instead of taking every value in one domain, only one test case is chosen from one class. In this way, testing covers the whole input domain, thereby reducing the total number of test cases. In fact, it is an attempt to get a good hit rate to find maximum errors with the smallest number of test cases.

Equivalence partitioning method for designing test cases has the following goals:

Completeness: Without executing all the test cases, we strive to touch the completeness of testing domain.

Non-redundancy: When the test cases are executed having inputs from the same class, then there is redundancy in executing the test cases. Time and resources are wasted in executing these redundant test cases, as they explore the same type of bug. Thus, the goal of equivalence partitioning method is to reduce these redundant test cases.

To use equivalence partitioning, one needs to perform the following two steps:

1. Identify equivalence classes
2. Design test cases