{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,28]],"date-time":"2025-08-28T12:24:28Z","timestamp":1756383868392,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":68,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,10]]},"DOI":"10.1145\/3551349.3556932","type":"proceedings-article","created":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T20:43:54Z","timestamp":1672951434000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Repairing Failure-inducing Inputs with Input Reflection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2563-083X","authenticated-orcid":false,"given":"Yan","family":"Xiao","sequence":"first","affiliation":[{"name":"Southeast University, China and National University of Singapore, Singapore"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1676-8834","authenticated-orcid":false,"given":"Yun","family":"Lin","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, China and National University of Singapore, Singapore"}]},{"given":"Ivan","family":"Beschastnikh","sequence":"additional","affiliation":[{"name":"University of British Columbia, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1685-4206","authenticated-orcid":false,"given":"Changsheng","family":"Sun","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}]},{"given":"David","family":"Rosenblum","sequence":"additional","affiliation":[{"name":"George Mason University, United States of America"}]},{"given":"Jin Song","family":"Dong","sequence":"additional","affiliation":[{"name":"National University of Singapore, Singapore"}]}],"member":"320","published-online":{"date-parts":[[2023,1,5]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSEA.2008.66"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0218001493000339"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00498"},{"key":"e_1_3_2_1_4_1","volume-title":"International Conference on Machine Learning. PMLR, 1670\u20131680","author":"Chen Lin","year":"2020","unstructured":"Lin Chen, Yifei Min, Mingrui Zhang, and Amin Karbasi. 2020. More data can expand the generalization gap between adversarially robust and standard models. In International Conference on Machine Learning. PMLR, 1670\u20131680."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.145"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Davide Chicco. 2021. Siamese neural networks: An overview. Artificial Neural Networks(2021) 73\u201394.","DOI":"10.1007\/978-1-0716-0826-5_3"},{"key":"e_1_3_2_1_7_1","volume-title":"International conference on machine learning. PMLR, 2206\u20132216","author":"Croce Francesco","year":"2020","unstructured":"Francesco Croce and Matthias Hein. 2020. Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. In International conference on machine learning. PMLR, 2206\u20132216."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Tommaso Dreossi Shromona Ghosh Xiangyu Yue Kurt Keutzer Alberto Sangiovanni-Vincentelli and Sanjit\u00a0A Seshia. 2018. Counterexample-guided data augmentation. arXiv preprint arXiv:1805.06962(2018).","DOI":"10.24963\/ijcai.2018\/286"},{"key":"e_1_3_2_1_9_1","volume-title":"International conference on machine learning. PMLR","author":"Engstrom Logan","year":"2019","unstructured":"Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, and Aleksander Madry. 2019. Exploring the landscape of spatial robustness. In International conference on machine learning. PMLR, 1802\u20131811."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2025113.2025179"},{"key":"e_1_3_2_1_11_1","volume-title":"1600 faults in 100 projects: automatically finding faults while achieving high coverage with evosuite. Empirical software engineering 20, 3","author":"Fraser Gordon","year":"2015","unstructured":"Gordon Fraser and Andrea Arcuri. 2015. 1600 faults in 100 projects: automatically finding faults while achieving high coverage with evosuite. Empirical software engineering 20, 3 (2015), 611\u2013639."},{"key":"e_1_3_2_1_12_1","volume-title":"Domain-adversarial training of neural networks. The journal of machine learning research 17, 1","author":"Ganin Yaroslav","year":"2016","unstructured":"Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, Fran\u00e7ois Laviolette, Mario Marchand, and Victor Lempitsky. 2016. Domain-adversarial training of neural networks. The journal of machine learning research 17, 1 (2016), 2096\u20132030."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377811.3380415"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/1065010.1065036"},{"key":"e_1_3_2_1_15_1","unstructured":"Ian\u00a0J Goodfellow Jonathon Shlens and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572(2014)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2019.07.016"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3236024.3264835"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3368089.3409754"},{"key":"e_1_3_2_1_19_1","unstructured":"Fengxiang He Shaopeng Fu Bohan Wang and Dacheng Tao. 2020. Robustness Privacy and Generalization of Adversarial Training. arXiv preprint arXiv:2012.13573(2020)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_21_1","unstructured":"Dan Hendrycks and Kevin Gimpel. 2016. A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136(2016)."},{"key":"e_1_3_2_1_22_1","unstructured":"Alexander Hermans Lucas Beyer and Bastian Leibe. 2017. In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737(2017)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01096"},{"key":"e_1_3_2_1_24_1","volume-title":"They Are Features. Advances in neural information processing systems 32","author":"Ilyas Andrew","year":"2019","unstructured":"Andrew Ilyas, Shibani Santurkar, Logan Engstrom, Brandon Tran, and Aleksander Madry. 2019. Adversarial Examples Are Not Bugs, They Are Features. Advances in neural information processing systems 32 (2019)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2007.02.015"},{"key":"e_1_3_2_1_26_1","volume-title":"Guiding Deep Learning System Testing Using Surprise Adequacy. In 2019 IEEE\/ACM 41st International Conference on Software Engineering (ICSE). IEEE, 1039\u20131049","author":"Kim Jinhan","year":"2019","unstructured":"Jinhan Kim, Robert Feldt, and Shin Yoo. 2019. Guiding Deep Learning System Testing Using Surprise Adequacy. In 2019 IEEE\/ACM 41st International Conference on Software Engineering (ICSE). IEEE, 1039\u20131049."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00330"},{"key":"e_1_3_2_1_28_1","volume-title":"2020 IEEE\/ACM 42nd International Conference on Software Engineering (ICSE). IEEE, 75\u201386","author":"Kirschner Lukas","year":"2020","unstructured":"Lukas Kirschner, Ezekiel Soremekun, and Andreas Zeller. 2020. Debugging inputs. In 2020 IEEE\/ACM 42nd International Conference on Software Engineering (ICSE). IEEE, 75\u201386."},{"key":"e_1_3_2_1_29_1","unstructured":"Gregory Koch Richard Zemel Ruslan Salakhutdinov 2015. Siamese neural networks for one-shot image recognition. In ICML deep learning workshop Vol.\u00a02. Lille."},{"key":"e_1_3_2_1_30_1","unstructured":"Alex Krizhevsky Geoffrey Hinton 2009. Learning Multiple Layers of Features from Tiny Images. (2009). https:\/\/www.cs.toronto.edu\/\u00a0kriz\/cifar.html"},{"key":"e_1_3_2_1_31_1","unstructured":"Yann LeCun Corinna Cortes and CJ Burges. 2010. MNIST Handwritten Digit Database. (2010). http:\/\/yann.lecun.com\/exdb\/mnist\/"},{"key":"e_1_3_2_1_32_1","volume-title":"32nd Conference on Neural Information Processing Systems (NIPS). Neural Information Processing Systems Foundation.","author":"Kibok Lee KIMIN LEE","year":"2018","unstructured":"KIMIN LEE, Kibok Lee, Honglak Lee, and Jinwoo Shin. 2018. A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks. In 32nd Conference on Neural Information Processing Systems (NIPS). Neural Information Processing Systems Foundation."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3395363.3397346"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00441"},{"key":"e_1_3_2_1_35_1","volume-title":"Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks. In International Conference on Learning Representations.","author":"Liang Shiyu","year":"2018","unstructured":"Shiyu Liang, Yixuan Li, and R Srikant. 2018. Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISSRE.2018.00021"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/1134285.1134307"},{"key":"e_1_3_2_1_39_1","unstructured":"Takeru Miyato Andrew\u00a0M Dai and Ian Goodfellow. 2016. Adversarial training methods for semi-supervised text classification. arXiv preprint arXiv:1605.07725(2016)."},{"key":"e_1_3_2_1_40_1","unstructured":"Yuval Netzer Tao Wang Adam Coates Alessandro Bissacco Bo Wu and Andrew\u00a0Y Ng. 2011. Reading digits in natural images with unsupervised feature learning. (2011)."},{"volume-title":"Companion to the 22nd ACM SIGPLAN conference on Object-oriented programming systems and applications companion. 815\u2013816.","author":"Pacheco Carlos","key":"e_1_3_2_1_41_1","unstructured":"Carlos Pacheco and Michael\u00a0D Ernst. 2007. Randoop: feedback-directed random testing for Java. In Companion to the 22nd ACM SIGPLAN conference on Object-oriented programming systems and applications companion. 815\u2013816."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2007.37"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132747.3132785"},{"volume-title":"Computer and Information Science","author":"Nguyen\u00a0Anh Pham Huy","key":"e_1_3_2_1_44_1","unstructured":"Huy Nguyen\u00a0Anh Pham and Evangelos Triantaphyllou. 2008. Prediction of diabetes by employing a new data mining approach which balances fitting and generalization. In Computer and Information Science. Springer, 11\u201326."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00655"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"e_1_3_2_1_47_1","volume-title":"Defense-gan: Protecting classifiers against adversarial attacks using generative models. arXiv preprint arXiv:1805.06605(2018).","author":"Samangouei Pouya","year":"2018","unstructured":"Pouya Samangouei, Maya Kabkab, and Rama Chellappa. 2018. Defense-gan: Protecting classifiers against adversarial attacks using generative models. arXiv preprint arXiv:1805.06605(2018)."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"e_1_3_2_1_49_1","volume-title":"Proceedings of the 33rd International Conference on Neural Information Processing Systems. 3358\u20133369","author":"Shafahi Ali","year":"2019","unstructured":"Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry\u00a0S Davis, Gavin Taylor, and Tom Goldstein. 2019. Adversarial training for free!. In Proceedings of the 33rd International Conference on Neural Information Processing Systems. 3358\u20133369."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.241"},{"key":"e_1_3_2_1_51_1","volume-title":"Very Deep Convolutional Networks for Large-scale Image Recognition. arXiv:1409.1556","author":"Simonyan Karen","year":"2014","unstructured":"Karen Simonyan and Andrew Zisserman. 2014. Very Deep Convolutional Networks for Large-scale Image Recognition. arXiv:1409.1556 (2014)."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.220"},{"key":"e_1_3_2_1_53_1","volume-title":"Visualizing data using t-SNE.Journal of machine learning research 9, 11","author":"Maaten Laurens Van\u00a0der","year":"2008","unstructured":"Laurens Van\u00a0der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE.Journal of machine learning research 9, 11 (2008)."},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377811.3380379"},{"key":"e_1_3_2_1_55_1","first-page":"7449","article-title":"Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free","volume":"33","author":"Wang N","year":"2020","unstructured":"Haotao\u00a0N Wang, Tianlong Chen, Shupeng Gui, TingKuei Hu, Ji Liu, and Zhangyang Wang. 2020. Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free. Advances in Neural Information Processing Systems 33 (2020), 7449\u20137461.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE43902.2021.00038"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00126"},{"key":"e_1_3_2_1_58_1","volume-title":"It\u2019s the Contents: Intra-domain Fingerprinting Social Media Websites Through CDN Bursts. In 30th The Web Conference (WWW).","author":"Wang Kailong","year":"2021","unstructured":"Kailong Wang, Junzhe Zhang, Guangdong Bai, Ryan Ko, and Jin\u00a0Song Dong. 2021. It\u2019s Not Just the Site, It\u2019s the Contents: Intra-domain Fingerprinting Social Media Websites Through CDN Bursts. In 30th The Web Conference (WWW)."},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"crossref","unstructured":"Kailong Wang Yuwei Zheng Qing Zhang Guangdong Bai Qin Mingchuang Donghui Zhang and Jin\u00a0Song Dong. 2022. Assessing Certificate Validation User Interfaces of WPA Supplicants. In MobiCom.","DOI":"10.1145\/3495243.3517026"},{"key":"e_1_3_2_1_60_1","volume-title":"Fakespotter: A simple yet robust baseline for spotting ai-synthesized fake faces. arXiv preprint arXiv:1909.06122(2019).","author":"Wang Run","year":"2019","unstructured":"Run Wang, Felix Juefei-Xu, Lei Ma, Xiaofei Xie, Yihao Huang, Jian Wang, and Yang Liu. 2019. Fakespotter: A simple yet robust baseline for spotting ai-synthesized fake faces. arXiv preprint arXiv:1909.06122(2019)."},{"key":"e_1_3_2_1_61_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_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2022.3200421"},{"key":"e_1_3_2_1_63_1","volume-title":"Self-Checking Deep Neural Networks in Deployment. In 2021 IEEE\/ACM 43rd International Conference on Software Engineering (ICSE). IEEE, 372\u2013384","author":"Xiao Yan","year":"2021","unstructured":"Yan Xiao, Ivan Beschastnikh, David\u00a0S Rosenblum, Changsheng Sun, Sebastian Elbaum, Yun Lin, and Jin\u00a0Song Dong. 2021. Self-Checking Deep Neural Networks in Deployment. In 2021 IEEE\/ACM 43rd International Conference on Software Engineering (ICSE). IEEE, 372\u2013384."},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00090"},{"key":"e_1_3_2_1_65_1","volume-title":"my program worked. Today, it does not. Why?ACM SIGSOFT Software engineering notes 24, 6","author":"Zeller Andreas","year":"1999","unstructured":"Andreas Zeller. 1999. Yesterday, my program worked. Today, it does not. Why?ACM SIGSOFT Software engineering notes 24, 6 (1999), 253\u2013267."},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377811.3380368"},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00472"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.7000"}],"event":{"name":"ASE '22: 37th IEEE\/ACM International Conference on Automated Software Engineering","acronym":"ASE '22","location":"Rochester MI USA"},"container-title":["Proceedings of the 37th IEEE\/ACM International Conference on Automated Software Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3551349.3556932","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3551349.3556932","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T08:30:55Z","timestamp":1755851455000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3551349.3556932"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":68,"alternative-id":["10.1145\/3551349.3556932","10.1145\/3551349"],"URL":"https:\/\/doi.org\/10.1145\/3551349.3556932","relation":{},"subject":[],"published":{"date-parts":[[2022,10,10]]},"assertion":[{"value":"2023-01-05","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}