  Testing practices within the machine learning (ML) community have centered
around assessing a learned model's predictive performance measured against a
test dataset, often drawn from the same distribution as the training dataset.
While recent work on robustness and fairness testing within the ML community
has pointed to the importance of testing against distributional shifts, these
efforts also focus on estimating the likelihood of the model making an error
against a reference dataset/distribution. We argue that this view of testing
actively discourages researchers and developers from looking into other sources
of robustness failures, for instance corner cases which may have severe
undesirable impacts. We draw parallels with decades of work within software
engineering testing focused on assessing a software system against various
stress conditions, including corner cases, as opposed to solely focusing on
average-case behaviour. Finally, we put forth a set of recommendations to
broaden the view of machine learning testing to a rigorous practice.
