  Deep learning (DL) plays a more and more important role in our daily life due
to its competitive performance in industrial application domains. As the core
of DL-enabled systems, deep neural networks (DNNs) need to be carefully
evaluated to ensure the produced models match the expected requirements. In
practice, the \emph{de facto standard} to assess the quality of DNNs in the
industry is to check their performance (accuracy) on a collected set of labeled
test data. However, preparing such labeled data is often not easy partly
because of the huge labeling effort, i.e., data labeling is labor-intensive,
especially with the massive new incoming unlabeled data every day. Recent
studies show that test selection for DNN is a promising direction that tackles
this issue by selecting minimal representative data to label and using these
data to assess the model. However, it still requires human effort and cannot be
automatic. In this paper, we propose a novel technique, named \textit{Aries},
that can estimate the performance of DNNs on new unlabeled data using only the
information obtained from the original test data. The key insight behind our
technique is that the model should have similar prediction accuracy on the data
which have similar distances to the decision boundary. We performed a
large-scale evaluation of our technique on two famous datasets, CIFAR-10 and
Tiny-ImageNet, four widely studied DNN models including ResNet101 and
DenseNet121, and 13 types of data transformation methods. Results show that the
estimated accuracy by \textit{Aries} is only 0.03\% -- 2.60\% off the true
accuracy. Besides, \textit{Aries} also outperforms the state-of-the-art
labeling-free methods in 50 out of 52 cases and selection-labeling-based
methods in 96 out of 128 cases.
