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testRNN: Coverage-guided Testing on Recurrent Neural Networks

arXiv 1906.08557 ↗First posted 20 Jun 2019cs.NE cs.LG cs.SEcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). First posted on arXiv 20 Jun 2019.

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