{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/testrnn-coverage-guided-testing-on-recurrent","title":"testRNN: Coverage-guided Testing on Recurrent Neural Networks","arxiv_id":"1906.08557","date":"2019-06-20","proceeding":null,"authors":["Wei Huang","Youcheng Sun","Xiaowei Huang","James Sharp"],"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.","url_abs":"https://arxiv.org/abs/1906.08557v1","url_pdf":"https://arxiv.org/pdf/1906.08557v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"testrnn-coverage-guided-testing-on-recurrent","repo_url":"https://github.com/TrustAI/testRNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"molecular-property-prediction","task_name":"Molecular Property Prediction"},{"task_slug":"property-prediction","task_name":"Property Prediction"},{"task_slug":"video-recognition","task_name":"Video Recognition"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}