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Conference Papers Year : 2019

Generating Biased Dataset for Metamorphic Testing of Machine Learning Programs

Abstract

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.
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hal-02526339 , version 1 (31-03-2020)

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Shin Nakajima, Tsong Yueh Chen. Generating Biased Dataset for Metamorphic Testing of Machine Learning Programs. 31th IFIP International Conference on Testing Software and Systems (ICTSS), Oct 2019, Paris, France. pp.56-64, ⟨10.1007/978-3-030-31280-0_4⟩. ⟨hal-02526339⟩
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