testRNN: Coverage-guided Testing on Recurrent Neural Networks

June 20, 2019 · Entered Twilight · 🏛 arXiv.org

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Authors Wei Huang, Youcheng Sun, Xiaowei Huang, James Sharp arXiv ID 1906.08557 Category cs.NE: Neural & Evolutionary Cross-listed cs.LG, cs.SE Citations 12 Venue arXiv.org Repository https://github.com/TrustAI/testRNN ⭐ 18 Last Checked 1 month ago

Abstract

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.