It is not easy to test software used in studies of machine learning with statistical frameworks. In particular, software for randomized algorithms such as Monte Carlo methods compromises testing process. Combined with underestimation of the importance of software testing in academic fields, many software programs without appropriate validation are being used and causing problems. In this article, we discuss the importance of writing test codes for software used in research, and present a practical way for testing, focusing on programs using Monte Carlo methods.
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