DeepMutation++: A mutation testing framework for deep learning systems

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

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

13 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトルProceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1158-1161
ページ数4
ISBN(電子版)9781728125084
DOI
出版ステータス出版済み - 11 2019
イベント34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019 - San Diego, 米国
継続期間: 11 10 201911 15 2019

出版物シリーズ

名前Proceedings - 2019 34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019

会議

会議34th IEEE/ACM International Conference on Automated Software Engineering, ASE 2019
国/地域米国
CitySan Diego
Period11/10/1911/15/19

All Science Journal Classification (ASJC) codes

  • コンピュータ ネットワークおよび通信
  • ソフトウェア
  • 制御と最適化

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