Although both positive and negative testing are important for assuring quality of programs, generating a variety of test inputs for such testing purposes is difficult for machine learning software. This paper studies why it is difficult, and then proposes a new method of generating datasets that are test inputs to machine learning programs. The proposed idea is demonstrated with a case study of classifying handwritten numbers.
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