一句话：提出支持前馈与循环神经网络的变异测试框架 DeepMutation++，用于评估 DNN 测试数据质量与模型鲁棒性。
深度神经网络在图像处理、语音识别和自然语言处理等领域应用日益广泛，但针对 DNN 测试的数据质量与模型鲁棒性仍缺乏工具支持。本文提出 DeepMutation++，一个基于变异测试的 DNN 测试工具，支持前馈神经网络（FNN）和有状态循环神经网络（RNN）。该工具既能静态分析整个 DNN 模型对输入的鲁棒性，也能通过运行时分析定位序列输入（如音频）中的脆弱片段，其特色在于特别支持 RNN 变异测试。工具演示视频见项目网站。
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.
@inproceedings{paperbot5260,
title = {DeepMutation++: A Mutation Testing Framework for Deep Learning Systems},
author = {Qiang Hu and Lei Ma and Xiaofei Xie and Bing Yu and Yang Liu and Jianjun Zhao},
booktitle = {ASE},
year = {2019},
doi = {10.1109/ase.2019.00126}
}