DEEPSTELLAR: MODEL-BASED QUANTITATIVE ANALYSIS OF STATEFUL DEEP LEARNING SYSTEMS
Xiaoning Du, Xiaofei Xie, Yi Li, Lei Ma, Yang Liu, and Jianjun Zhao
In Proceedings of the 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, FSE 2019
Abstract: Deep Learning (DL) has achieved tremendous success in many cutting-edge applications. However, the state-of-the-art DL systems still suffer from many quality issues. While some recent progress has been made on the analysis of feed-forward DL systems, little study has been done on the Recurrent Neural Network (RNN)-based stateful DL systems, which are widely used in audio, natural languages and video processing. In this paper, we initiate the very first step towards the quantitative analysis of RNN-based DL systems. We model RNN as an abstract state transition system to characterize its internal behaviors. Based on the abstract model, we then design two trace similarity metrics and five testing criteria which enable the quantitative analysis of RNNs. We further propose two algorithms powered by the quantitative measures for adversarial sample detection and coverage-guided test generation. We evaluate DeepStellar on four RNN-based systems covering image classification and automated speech recognition. The results demonstrate that the abstract model is useful in characterizing the internal behaviors of RNNs, and further confirm that (1) the similarity metrics could effectively capture the differences even when the samples have a very small perturbation (achieving 93% accuracy for detecting adversarial samples of RNNs) and (2) the coverage criteria are useful in revealing erroneous behaviors (generating three times more adversarial samples than random testing).