Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

11-2019

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 static analysis of the robustness of a DNN model against the input as a whole, but also allows the identification of 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.

Discipline

OS and Networks | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 34th IEEE/ACM International Conference on Automated Software Engineering, San Diego, 2019 November 11-15

First Page

1158

Last Page

1161

ISBN

9781728125084

Identifier

10.1109/ASE.2019.00126

Publisher

IEEE

City or Country

San Diego, California

Download

Share

COinS