  This paper demonstrates the systematic use of combinatorial coverage for
selecting and characterizing test and training sets for machine learning
models. The presented work adapts combinatorial interaction testing, which has
been successfully leveraged in identifying faults in software testing, to
characterize data used in machine learning. The MNIST hand-written digits data
is used to demonstrate that combinatorial coverage can be used to select test
sets that stress machine learning model performance, to select training sets
that lead to robust model performance, and to select data for fine-tuning
models to new domains. Thus, the results posit combinatorial coverage as a
holistic approach to training and testing for machine learning. In contrast to
prior work which has focused on the use of coverage in regard to the internal
of neural networks, this paper considers coverage over simple features derived
from inputs and outputs. Thus, this paper addresses the case where the supplier
of test and training sets for machine learning models does not have
intellectual property rights to the models themselves. Finally, the paper
addresses prior criticism of combinatorial coverage and provides a rebuttal
which advocates the use of coverage metrics in machine learning applications.
