  Testing deep learning-based systems is crucial but challenging due to the
required time and labor for labeling collected raw data. To alleviate the
labeling effort, multiple test selection methods have been proposed where only
a subset of test data needs to be labeled while satisfying testing
requirements. However, we observe that such methods with reported promising
results are only evaluated under simple scenarios, e.g., testing on original
test data. This brings a question to us: are they always reliable? In this
paper, we explore when and to what extent test selection methods fail for
testing. Specifically, first, we identify potential pitfalls of 11 selection
methods from top-tier venues based on their construction. Second, we conduct a
study on five datasets with two model architectures per dataset to empirically
confirm the existence of these pitfalls. Furthermore, we demonstrate how
pitfalls can break the reliability of these methods. Concretely, methods for
fault detection suffer from test data that are: 1) correctly classified but
uncertain, or 2) misclassified but confident. Remarkably, the test relative
coverage achieved by such methods drops by up to 86.85%. On the other hand,
methods for performance estimation are sensitive to the choice of
intermediate-layer output. The effectiveness of such methods can be even worse
than random selection when using an inappropriate layer.
