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                  <title>31th IFIP International Conference on Testing Software and Systems (ICTSS)</title>
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                  <date type="end">2019-10-17</date>
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                <editor>Nikolai Kosmatov</editor>
                <editor>Pascale Le Gall</editor>
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              <p>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 hand-written numbers.</p>
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