{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T11:24:57Z","timestamp":1762341897924,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":66,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,5,21]],"date-time":"2022-05-21T00:00:00Z","timestamp":1653091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62002158, 61832009, 61932012"],"award-info":[{"award-number":["62002158, 61832009, 61932012"]}]},{"name":"Science, Technology, and Innovation Commission of Shenzhen Municipality","award":["CJGJZD20200617103001003"],"award-info":[{"award-number":["CJGJZD20200617103001003"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,5,21]]},"DOI":"10.1145\/3510003.3510231","type":"proceedings-article","created":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T22:42:59Z","timestamp":1657060979000},"page":"598-609","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["DeepState"],"prefix":"10.1145","author":[{"given":"Zixi","family":"Liu","sequence":"first","affiliation":[{"name":"Nanjing University, Nanjing, China"}]},{"given":"Yang","family":"Feng","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}]},{"given":"Yining","family":"Yin","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}]},{"given":"Zhenyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Nanjing University, Nanjing, China"}]}],"member":"320","published-online":{"date-parts":[[2022,7,5]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"[n.d.]. AG's corpus of news articles. http:\/\/groups.di.unipi.it\/~gulli\/AG_corpus_of_news_articles.html. (Accessed on 08\/21\/2021)."},{"key":"e_1_3_2_1_2_1","unstructured":"[n.d.]. Amazon promises fix for creepy Alexa laugh - BBC News. https:\/\/www.bbc.com\/news\/technology-43325230. (Accessed on 08\/23\/2021)."},{"key":"e_1_3_2_1_3_1","unstructured":"[n.d.]. Amazon promises fix for creepy Alexa laugh - BBC News. https:\/\/www.bbc.com\/news\/technology-43325230. (Accessed on 08\/29\/2021)."},{"key":"e_1_3_2_1_4_1","unstructured":"[n.d.]. Python Release Python 3.6.0 | Python.org. https:\/\/www.python.org\/downloads\/release\/python-360\/. (Accessed on 07\/10\/2021)."},{"key":"e_1_3_2_1_5_1","unstructured":"[n.d.]. TensorFlow. https:\/\/www.tensorflow.org\/. (Accessed on 08\/23\/2021)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"e_1_3_2_1_7_1","volume-title":"Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473","author":"Bahdanau Dzmitry","year":"2014","unstructured":"Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)."},{"key":"e_1_3_2_1_8_1","volume-title":"Enhanced lstm for natural language inference. arXiv preprint arXiv:1609.06038","author":"Chen Qian","year":"2016","unstructured":"Qian Chen, Xiaodan Zhu, Zhenhua Ling, Si Wei, Hui Jiang, and Diana Inkpen. 2016. Enhanced lstm for natural language inference. arXiv preprint arXiv:1609.06038 (2016)."},{"key":"e_1_3_2_1_9_1","unstructured":"Fran\u00e7ois Chollet et al. 2015. Keras. https:\/\/keras.io."},{"key":"e_1_3_2_1_10_1","volume-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555","author":"Chung Junyoung","year":"2014","unstructured":"Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)."},{"key":"e_1_3_2_1_11_1","unstructured":"Alice Coucke Alaa Saade Adrien Ball Th\u00e9odore Bluche Alexandre Caulier David Leroy Cl\u00e9ment Doumouro Thibault Gisselbrecht Francesco Caltagirone Thibaut Lavril et al. 2018. Snips voice platform: an embedded spoken language understanding system for private-by-design voice interfaces. arXiv preprint arXiv:1805.10190 (2018)."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09866-x"},{"key":"e_1_3_2_1_13_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/MWSCAS.2017.8053243"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3338906.3338954"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3395363.3397357"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2005.1556215"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/367008.367020"},{"key":"e_1_3_2_1_19_1","volume-title":"Rnn-test: Adversarial testing framework for recurrent neural network systems. arXiv preprint arXiv:1911.06155","author":"Guo Jianmin","year":"2019","unstructured":"Jianmin Guo, Yue Zhao, Xueying Han, Yu Jiang, and Jiaguang Sun. 2019. Rnn-test: Adversarial testing framework for recurrent neural network systems. arXiv preprint arXiv:1911.06155 (2019)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3409754"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2008.4563068"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASE.2019.00126"},{"key":"e_1_3_2_1_24_1","volume-title":"testrnn: Coverage-guided testing on recurrent neural networks. arXiv preprint arXiv:1906.08557","author":"Huang Wei","year":"2019","unstructured":"Wei Huang, Youcheng Sun, Xiaowei Huang, and James Sharp. 2019. testrnn: Coverage-guided testing on recurrent neural networks. arXiv preprint arXiv:1906.08557 (2019)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/TR.2021.3080664"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206627"},{"key":"e_1_3_2_1_27_1","volume-title":"Contextual augmentation: Data augmentation by words with paradigmatic relations. arXiv preprint arXiv:1805.06201","author":"Kobayashi Sosuke","year":"2018","unstructured":"Sosuke Kobayashi. 2018. Contextual augmentation: Data augmentation by words with paradigmatic relations. arXiv preprint arXiv:1805.06201 (2018)."},{"key":"e_1_3_2_1_28_1","unstructured":"John Lafferty Andrew McCallum and Fernando CN Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. (2001)."},{"key":"e_1_3_2_1_29_1","volume-title":"Deep learning. nature 521, 7553","author":"LeCun Yann","year":"2015","unstructured":"Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. nature 521, 7553 (2015), 436--444."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4471-2099-5_1"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE-NIER.2019.00031"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1080\/01431160600746456"},{"key":"e_1_3_2_1_33_1","unstructured":"Edward Ma. 2019. NLP Augmentation. https:\/\/github.com\/makcedward\/nlpaug."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3238147.3238202"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISSRE.2018.00021"},{"key":"e_1_3_2_1_36_1","volume-title":"Proceedings","volume":"89","author":"Malhotra Pankaj","year":"2015","unstructured":"Pankaj Malhotra, Lovekesh Vig, Gautam Shroff, and Puneet Agarwal. 2015. Long short term memory networks for anomaly detection in time series. In Proceedings, Vol. 89. Presses universitaires de Louvain, 89--94."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2011.5947611"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/IIPHDW.2018.8388338"},{"key":"e_1_3_2_1_39_1","volume-title":"Proceedings of the international multiconference of engineers and computer scientists","volume":"1","author":"Niwattanakul Suphakit","year":"2013","unstructured":"Suphakit Niwattanakul, Jatsada Singthongchai, Ekkachai Naenudorn, and Supachanun Wanapu. 2013. Using of Jaccard coefficient for keywords similarity. In Proceedings of the international multiconference of engineers and computer scientists, Vol. 1. 380--384."},{"volume-title":"Markov chains. Number 2","author":"Norris James R","key":"e_1_3_2_1_40_1","unstructured":"James R Norris and John Robert Norris. 1998. Markov chains. Number 2. Cambridge university press."},{"key":"e_1_3_2_1_41_1","unstructured":"Christopher Olah. 2015. Understanding lstm networks. (2015)."},{"key":"e_1_3_2_1_42_1","unstructured":"Tom O'Malley Elie Bursztein James Long Fran\u00e7ois Chollet Haifeng Jin Luca Invernizzi et al. 2019. Keras Tuner. https:\/\/github.com\/keras-team\/keras-tuner."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3361566"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1090\/proc\/14789"},{"key":"e_1_3_2_1_45_1","volume-title":"Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683","author":"Raffel Colin","year":"2019","unstructured":"Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2019. Exploring the limits of transfer learning with a unified text-to-text transformer. arXiv preprint arXiv:1910.10683 (2019)."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447582"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/32.536955"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/248233.248262"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2015.7178838"},{"key":"e_1_3_2_1_50_1","volume-title":"Fully connected deep structured networks. arXiv preprint arXiv:1503.02351","author":"Schwing Alexander G","year":"2015","unstructured":"Alexander G Schwing and Raquel Urtasun. 2015. Fully connected deep structured networks. arXiv preprint arXiv:1503.02351 (2015)."},{"key":"e_1_3_2_1_51_1","volume-title":"Improving neural machine translation models with monolingual data. arXiv preprint arXiv:1511.06709","author":"Sennrich Rico","year":"2015","unstructured":"Rico Sennrich, Barry Haddow, and Alexandra Birch. 2015. Improving neural machine translation models with monolingual data. arXiv preprint arXiv:1511.06709 (2015)."},{"key":"e_1_3_2_1_52_1","unstructured":"Burr Settles. 2009. Active learning literature survey. (2009)."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physd.2019.132306"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"e_1_3_2_1_55_1","volume-title":"Introduction to multilayer feed-forward neural networks. Chemometrics and intelligent laboratory systems 39, 1","author":"Svozil Daniel","year":"1997","unstructured":"Daniel Svozil, Vladimir Kvasnicka, and Jiri Pospichal. 1997. Introduction to multilayer feed-forward neural networks. Chemometrics and intelligent laboratory systems 39, 1 (1997), 43--62."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/3180155.3180220"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE43902.2021.00046"},{"key":"e_1_3_2_1_58_1","unstructured":"Kashif Rasul & Han Xiao. 2017. Fashion. https:\/\/research.zalando.com\/welcome\/mission\/research-projects\/fashion-mnist\/."},{"key":"e_1_3_2_1_59_1","volume-title":"Achieving human parity in conversational speech recognition. arXiv preprint arXiv:1610.05256","author":"Xiong Wayne","year":"2016","unstructured":"Wayne Xiong, Jasha Droppo, Xuedong Huang, Frank Seide, Mike Seltzer, Andreas Stolcke, Dong Yu, and Geoffrey Zweig. 2016. Achieving human parity in conversational speech recognition. arXiv preprint arXiv:1610.05256 (2016)."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3409671"},{"key":"e_1_3_2_1_61_1","volume-title":"Corinna Cortes.","author":"Christopher","year":"1998","unstructured":"Christopher J.C. Burges Yann LeCun, Corinna Cortes. 1998. MNIST. http:\/\/yann.lecun.com\/exdb\/mnist\/."},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1002\/stv.430"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2886017"},{"key":"e_1_3_2_1_64_1","volume-title":"Recurrent neural network regularization. arXiv preprint arXiv:1409.2329","author":"Zaremba Wojciech","year":"2014","unstructured":"Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals. 2014. Recurrent neural network regularization. arXiv preprint arXiv:1409.2329 (2014)."},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3180155.3180198"},{"key":"e_1_3_2_1_66_1","volume-title":"Proceedings of the 28th International Conference on Neural Information Processing Systems -","volume":"1","author":"Zhang Xiang","year":"2015","unstructured":"Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015. Character-Level Convolutional Networks for Text Classification. In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1 (Montreal, Canada) (NIPS'15). MIT Press, Cambridge, MA, USA, 649--657."}],"event":{"name":"ICSE '22: 44th International Conference on Software Engineering","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering","IEEE CS"],"location":"Pittsburgh Pennsylvania","acronym":"ICSE '22"},"container-title":["Proceedings of the 44th International Conference on Software Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3510003.3510231","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3510003.3510231","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:12:24Z","timestamp":1750191144000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3510003.3510231"}},"subtitle":["selecting test suites to enhance the robustness of recurrent neural networks"],"short-title":[],"issued":{"date-parts":[[2022,5,21]]},"references-count":66,"alternative-id":["10.1145\/3510003.3510231","10.1145\/3510003"],"URL":"https:\/\/doi.org\/10.1145\/3510003.3510231","relation":{},"subject":[],"published":{"date-parts":[[2022,5,21]]},"assertion":[{"value":"2022-07-05","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}