Recurrent neural networks (RNNs) have been widely applied to various sequential tasks such as text processing, video recognition, and molecular property prediction. We introduce the first coverage-guided testing tool, coined testRNN, for the verification and validation of a major class of RNNs, long short-term memory networks (LSTMs). The tool implements a generic mutation-based test case generation method, and it empirically evaluates the robustness of a network using three novel LSTM structural test coverage metrics. Moreover, it is able to help the model designer go through the internal data flow processing of the LSTM layer. The tool is available through: https://github.com/TrustAI/testRNN under the BSD 3-Clause licence.
@article{arxiv.1906.08557,
title = {testRNN: Coverage-guided Testing on Recurrent Neural Networks},
author = {Wei Huang and Youcheng Sun and Xiaowei Huang and James Sharp},
journal= {arXiv preprint arXiv:1906.08557},
year = {2019}
}