DeepMutation++: A Mutation Testing Framework for Deep Learning Systems

Qiang Hu, Lei Ma, Xiaofei Xie, Bing Yu, Yang Liu, Jianjun Zhao

Abstract: Deep neural networks (DNNs) are increasingly expanding their real-world applications across domains, e.g., image processing, speech recognition and natural language processing. However, there is still limited tool support for DNN testing in terms of test data quality and model robustness. In this paper, we introduce a mutation testing-based tool for DNNs, DeepMutation++, which facilitates the DNN quality evaluation, supporting both feed-forward neural networks (FNNs) and stateful recurrent neural networks (RNNs). It not only enables to statically analyze the robustness of a DNN model against the input as a whole, but also allows to identify the vulnerable segments of a sequential input (e.g. audio input) by runtime analysis. It is worth noting that DeepMutation++ specially features the support of RNNs mutation testing. The tool demo video can be found on the project website https://sites.google.com/view/deepmutationpp.