Given a program p and a specification *R* whose domain is *X*, we are interested to generate test data *T* as a subset of *X* in such a way that when we execute the candidate program p on all the elements of set *T* and observe its behavior, we can make meaningful inference on the functional properties of program p. In Chapter 8, we have discussed the requirements that set *T* must satisfy, depending on the goal of testing, and we have analyzed possible generation criteria that yield such data sets. The generation of test data may proceed either by analyzing the specification of the program or by analyzing the source code of the program. In this chapter, we focus on the first approach.

The criterion of *domain partitioning* is based on the premise that the input space *X* of the specification can be partitioned into equivalence classes such that in each class, candidate programs either runs successfully on all the states in the class or fails on all the states in the class; this criterion may be rationalized if we define equivalence classes to include all the input data that are processed the same way. Under such a condition, we can let *T* be a subset of *S* that includes a single element of each equivalence class. The selection criterion on a test set *T* can be formulated as follows:

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