Generating Biased Dataset for Metamorphic Testing of Machine Learning Programs
Generating Biased Dataset for Metamorphic Testing of Machine Learning Programs
复制标题
为机器学习程序的变形测试生成有偏差的数据集
DOI：
10.1007/978-3-030-31280-0_4
复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
T.Y. Chen
中科院分区：
文献类型：
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作者：
Shin Nakajima;T.Y. Chen
虽然肯定测试和否定测试对于确保程序质量都很重要，但为这样的测试目的生成各种测试输入对于机器学习软件来说是困难的。本文研究了这一困难的原因，并提出了一种新的生成数据集的方法，这些数据集是机器学习程序的测试输入。通过对手写数字分类的实例研究，验证了该方法的有效性。
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.