{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T23:31:58Z","timestamp":1769729518604,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T00:00:00Z","timestamp":1726012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Key R&D Program of Zhejiang","award":["2022C01018"],"award-info":[{"award-number":["2022C01018"]}]},{"name":"National Science Foundation of China","award":["62102359, 62293511, 62088101"],"award-info":[{"award-number":["62102359, 62293511, 62088101"]}]},{"name":"China Scholarship Council","award":["202306320425"],"award-info":[{"award-number":["202306320425"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,9,11]]},"DOI":"10.1145\/3650212.3652132","type":"proceedings-article","created":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T11:44:25Z","timestamp":1726055065000},"page":"338-349","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Isolation-Based Debugging for Neural Networks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4322-4285","authenticated-orcid":false,"given":"Jialuo","family":"Chen","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7113-7635","authenticated-orcid":false,"given":"Jingyi","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1893-6259","authenticated-orcid":false,"given":"Youcheng","family":"Sun","sequence":"additional","affiliation":[{"name":"University of Manchester, Manchester, United Kingdom"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4221-2162","authenticated-orcid":false,"given":"Peng","family":"Cheng","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3155-3145","authenticated-orcid":false,"given":"Jiming","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,9,11]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"[n. d.]. https:\/\/github.com\/Testing4AI\/IDNN."},{"key":"e_1_3_2_1_2_1","first-page":"3971","volume-title":"31st USENIX Security Symposium (USENIX Security 22)","author":"Arp Daniel","year":"2022","unstructured":"Daniel Arp, Erwin Quiring, Feargus Pendlebury, Alexander Warnecke, Fabio Pierazzi, Christian Wressnegger, Lorenzo Cavallaro, and Konrad Rieck. 2022. Dos and don'ts of machine learning in computer security. In 31st USENIX Security Symposium (USENIX Security 22). 3971-3988."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2525528.2525533"},{"key":"e_1_3_2_1_4_1","unstructured":"Arthur Asuncion and David Newman. 2007. UCI machine learning repository."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3338906.3338957"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3582573"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1062455.1062522"},{"key":"e_1_3_2_1_8_1","unstructured":"Ronan Collobert Jason Weston L\u00e9on Bottou Michael Karlen Koray Kavukcuoglu and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. Journal of machine learning research 12 ARTICLE ( 2011 ) 2493-2537."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICECCS51672.2020.00016"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-16722-6_10"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3395363.3397357"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3106237.3106277"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Robert Geirhos J\u00f6rn-Henrik Jacobsen Claudio Michaelis Richard Zemel Wieland Brendel Matthias Bethge and Felix A Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2 11 ( 2020 ) 665-673.","DOI":"10.1038\/s42256-020-00257-z"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Robert Geirhos J\u00f6rn-Henrik Jacobsen Claudio Michaelis Richard Zemel Wieland Brendel Matthias Bethge and Felix A Wichmann. 2020. Shortcut learning in deep neural networks. Nature Machine Intelligence 2 11 ( 2020 ) 665-673.","DOI":"10.1038\/s42256-020-00257-z"},{"key":"e_1_3_2_1_15_1","volume-title":"LPAR","volume":"2020","author":"Goldberger Ben","year":"2020","unstructured":"Ben Goldberger, Guy Katz, Yossi Adi, and Joseph Keshet. 2020. Minimal Modifications of Deep Neural Networks using Verification.. In LPAR, Vol. 2020. 23rd."},{"key":"e_1_3_2_1_16_1","volume-title":"Deep learning","author":"Goodfellow Ian","unstructured":"Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep learning. MIT press."},{"key":"e_1_3_2_1_17_1","volume-title":"Badnets: Evaluating backdooring attacks on deep neural networks","author":"Gu Tianyu","year":"2019","unstructured":"Tianyu Gu, Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg. 2019. Badnets: Evaluating backdooring attacks on deep neural networks. IEEE Access 7 ( 2019 ), 47230-47244."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3236024.3264835"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00108"},{"key":"e_1_3_2_1_20_1","first-page":"202","article-title":"Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid","volume":"96","author":"Ron Kohavi","year":"1996","unstructured":"Ron Kohavi et al. 1996. Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid.. In Kdd, Vol. 96. 202-207.","journal-title":"Kdd"},{"key":"e_1_3_2_1_21_1","unstructured":"Alex Krizhevsky Geofrey Hinton et al. 2009. Learning multiple layers of features from tiny images. ( 2009 )."},{"key":"e_1_3_2_1_22_1","first-page":"2278","volume-title":"Proc. IEEE 86","author":"LeCun Yann","year":"1998","unstructured":"Yann LeCun, L\u00e9on Bottou, Yoshua Bengio, and Patrick Hafner. 1998. Gradientbased learning applied to document recognition. Proc. IEEE 86, 11 ( 1998 ), 2278-2324."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00470-5_13"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2018.23291"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3238147.3238202"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3236024.3236082"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/1134285.1134307"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132747.3132785"},{"key":"e_1_3_2_1_29_1","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 ( 2014 )."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3563210"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3563210"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3453483.3454064"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510080"},{"key":"e_1_3_2_1_34_1","volume-title":"Computer vision: algorithms and applications","author":"Szeliski Richard","unstructured":"Richard Szeliski. 2010. Computer vision: algorithms and applications. Springer Science & Business Media."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3180155.3180220"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377811.3380400"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3238147.3238165"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-81685-8_1"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00031"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3468264.3468625"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE43902.2021.00038"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.1984.5010248"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2016.2521368"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.5555\/3104322.3104470"},{"key":"e_1_3_2_1_45_1","unstructured":"Han Xiao Kashif Rasul and Roland Vollgraf. 2017. Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 ( 2017 )."},{"key":"e_1_3_2_1_46_1","article-title":"Deeprepair: Style-guided repairing for deep neural networks in the real-world operational environment","volume":"71","author":"Yu Bing","year":"2021","unstructured":"Bing Yu, Hua Qi, Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, and Jianjun Zhao. 2021. Deeprepair: Style-guided repairing for deep neural networks in the real-world operational environment. IEEE Transactions on Reliability 71, 4 ( 2021 ), 1401-1416.","journal-title":"IEEE Transactions on Reliability"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1972.10481251"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASE.2019.00043"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377811.3380331"}],"event":{"name":"ISSTA '24: 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis","location":"Vienna Austria","acronym":"ISSTA '24","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering","AITO"]},"container-title":["Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3650212.3652132","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3650212.3652132","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:50:06Z","timestamp":1750287006000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3650212.3652132"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,11]]},"references-count":49,"alternative-id":["10.1145\/3650212.3652132","10.1145\/3650212"],"URL":"https:\/\/doi.org\/10.1145\/3650212.3652132","relation":{},"subject":[],"published":{"date-parts":[[2024,9,11]]},"assertion":[{"value":"2024-09-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}