{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T21:51:48Z","timestamp":1783288308008,"version":"3.54.6"},"publisher-location":"New York, NY, USA","reference-count":42,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,6,27]],"date-time":"2020-06-27T00:00:00Z","timestamp":1593216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100007515","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1513263, 1934884"],"award-info":[{"award-number":["1513263, 1934884"]}],"id":[{"id":"10.13039\/100007515","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,6,27]]},"DOI":"10.1145\/3377811.3380378","type":"proceedings-article","created":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T18:25:34Z","timestamp":1601576734000},"page":"1135-1146","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":93,"title":["Repairing deep neural networks"],"prefix":"10.1145","author":[{"given":"Md Johirul","family":"Islam","sequence":"first","affiliation":[{"name":"Iowa State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rangeet","family":"Pan","sequence":"additional","affiliation":[{"name":"Iowa State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giang","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Iowa State University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hridesh","family":"Rajan","sequence":"additional","affiliation":[{"name":"Iowa State University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,10]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1882291.1882308"},{"key":"e_1_3_2_1_2_1","volume-title":"Poisoning attacks against support vector machines. arXiv preprint arXiv:1206.6389","author":"Biggio Battista","year":"2012","unstructured":"Battista Biggio, Blaine Nelson, and Pavel Laskov. 2012. Poisoning attacks against support vector machines. arXiv preprint arXiv:1206.6389 (2012)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/SANER.2018.8330214"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"e_1_3_2_1_5_1","volume-title":"Targeted backdoor attacks on deep learning systems using data poisoning. arXiv preprint arXiv:1712.05526","author":"Chen Xinyun","year":"2017","unstructured":"Xinyun Chen, Chang Liu, Bo Li, Kimberly Lu, and Dawn Song. 2017. Targeted backdoor attacks on deep learning systems using data poisoning. arXiv preprint arXiv:1712.05526 (2017)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/502034.502042"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/CSMR-WCRE.2014.6747226"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2015.04.066"},{"key":"e_1_3_2_1_9_1","volume-title":"On bias, variance, 0\/1-loss, and the curse-of-dimensionality. Data mining and knowledge discovery 1, 1","author":"Friedman Jerome H","year":"1997","unstructured":"Jerome H Friedman. 1997. On bias, variance, 0\/1-loss, and the curse-of-dimensionality. Data mining and knowledge discovery 1, 1 (1997), 55--77."},{"key":"e_1_3_2_1_10_1","volume-title":"Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572","author":"Goodfellow Ian J","year":"2014","unstructured":"Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3338906.3338955"},{"key":"e_1_3_2_1_12_1","volume-title":"Rangeet Pan, and Hridesh Rajan.","author":"Islam Md Johirul","year":"2019","unstructured":"Md Johirul Islam, Hoan Anh Nguyen, Rangeet Pan, and Hridesh Rajan. 2019. What Do Developers Ask About ML Libraries? A Large-scale Study Using Stack Overflow. arXiv preprint arXiv:1906.11940 (2019)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Md Johirul Islam Rangeet Pan Giang Nguyen and Hridesh Rajan. 2020. A Benchmark for Bugs and Fix Patterns for Deep Neural Networks. https:\/\/github.com\/lab-design\/ICSE2020DNNBugRepair.","DOI":"10.1145\/3377811.3380378"},{"key":"e_1_3_2_1_14_1","volume-title":"On loss functions for deep neural networks in classification. arXiv preprint arXiv:1702.05659","author":"Janocha Katarzyna","year":"2017","unstructured":"Katarzyna Janocha and Wojciech Marian Czarnecki. 2017. On loss functions for deep neural networks in classification. arXiv preprint arXiv:1702.05659 (2017)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2015.02.014"},{"key":"e_1_3_2_1_16_1","volume-title":"Auto-keras: Efficient neural architecture search with network morphism. arXiv preprint arXiv:1806.10282","author":"Jin Haifeng","year":"2018","unstructured":"Haifeng Jin, Qingquan Song, and Xia Hu. 2018. Auto-keras: Efficient neural architecture search with network morphism. arXiv preprint arXiv:1806.10282 (2018)."},{"key":"e_1_3_2_1_17_1","volume-title":"Principal component analysis","author":"Jolliffe Ian","unstructured":"Ian Jolliffe. 2011. Principal component analysis. Springer."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1181309.1181314"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/1346281.1346323"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2017.42"},{"key":"e_1_3_2_1_21_1","first-page":"2579","article-title":"Visualizing data using t-SNE","author":"van der Maaten Laurens","year":"2008","unstructured":"Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research 9, Nov (2008), 2579--2605.","journal-title":"Journal of machine learning research 9"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2635868.2635924"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-008-9077-5"},{"key":"e_1_3_2_1_24_1","volume-title":"Shibbir Ahmed, and Hridesh Rajan.","author":"Pan Rangeet","year":"2019","unstructured":"Rangeet Pan, Md Johirul Islam, Shibbir Ahmed, and Hridesh Rajan. 2019. Identifying Classes Susceptible to Adversarial Attacks. arXiv preprint arXiv:1905.13284 (2019)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.5555\/2664446.2664453"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00107"},{"key":"e_1_3_2_1_27_1","volume-title":"Technical Report","author":"Rajan Hridesh","unstructured":"Hridesh Rajan. 2020. D4 (Dependable Data Driven Discovery) Framework. Technical Report. Iowa State University."},{"key":"e_1_3_2_1_28_1","volume-title":"2018 IEEE\/ACM 15th International Conference on Mining Software Repositories (MSR). IEEE, 10--13","author":"Saha Ripon","year":"2018","unstructured":"Ripon Saha, Yingjun Lyu, Wing Lam, Hiroaki Yoshida, and Mukul Prasad. 2018. Bugs. jar: A large-scale, diverse dataset of real-world Java bugs. In 2018 IEEE\/ACM 15th International Conference on Mining Software Repositories (MSR). IEEE, 10--13."},{"key":"e_1_3_2_1_29_1","volume-title":"Effects of Loss Functions And Target Representations on Adversarial Robustness. arXiv preprint arXiv:1812.00181","author":"Saito Sean","year":"2018","unstructured":"Sean Saito and Sujoy Roy. 2018. Effects of Loss Functions And Target Representations on Adversarial Robustness. arXiv preprint arXiv:1812.00181 (2018)."},{"key":"e_1_3_2_1_30_1","unstructured":"David Sculley Gary Holt Daniel Golovin Eugene Davydov Todd Phillips Dietmar Ebner Vinay Chaudhary and Michael Young. 2014. Machine learning: The high interest credit card of technical debt. (2014)."},{"key":"e_1_3_2_1_31_1","volume-title":"Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1","author":"Srivastava Nitish","year":"2014","unstructured":"Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15, 1 (2014), 1929--1958."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/APSEC.2017.41"},{"key":"e_1_3_2_1_33_1","unstructured":"TensorFlow. 2019. TensorFlow Github. https:\/\/github.com\/tensorflow\/tensorflow\/tags\/."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISSRE.2012.22"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.5555\/2337223.2337318"},{"key":"e_1_3_2_1_36_1","unstructured":"Anthony J Viera Joanne M Garrett et al. 2005. Understanding interobserver agreement: the kappa statistic. Fam med 37 5 (2005) 360--363."},{"key":"e_1_3_2_1_37_1","volume-title":"Wolf in Sheep's Clothing-The Downscaling Attack Against Deep Learning Applications. arXiv preprint arXiv:1712.07805","author":"Xiao Qixue","year":"2017","unstructured":"Qixue Xiao, Kang Li, Deyue Zhang, and Yier Jin. 2017. Wolf in Sheep's Clothing-The Downscaling Attack Against Deep Learning Applications. arXiv preprint arXiv:1712.07805 (2017)."},{"key":"e_1_3_2_1_38_1","volume-title":"Mitigating adversarial effects through randomization. arXiv preprint arXiv:1711.01991","author":"Xie Cihang","year":"2017","unstructured":"Cihang Xie, Jianyu Wang, Zhishuai Zhang, Zhou Ren, and Alan Yuille. 2017. Mitigating adversarial effects through randomization. arXiv preprint arXiv:1711.01991 (2017)."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/1985441.1985457"},{"key":"e_1_3_2_1_40_1","volume-title":"An Empirical Study of Common Challenges in Developing Deep Learning Applications. In 2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE). IEEE.","author":"Zhang Tianyi","year":"2019","unstructured":"Tianyi Zhang, Cuiyun Gao, Lei Ma, Michael R. Lyu, and Miryung Kim. 2019. An Empirical Study of Common Challenges in Developing Deep Learning Applications. In 2019 IEEE 30th International Symposium on Software Reliability Engineering (ISSRE). IEEE."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3213846.3213866"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.5555\/2818754.2818864"}],"event":{"name":"ICSE '20: 42nd International Conference on Software Engineering","location":"Seoul South Korea","acronym":"ICSE '20","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering","KIISE Korean Institute of Information Scientists and Engineers","IEEE CS"]},"container-title":["Proceedings of the ACM\/IEEE 42nd International Conference on Software Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3377811.3380378","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3377811.3380378","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:41:39Z","timestamp":1750200099000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3377811.3380378"}},"subtitle":["fix patterns and challenges"],"short-title":[],"issued":{"date-parts":[[2020,6,27]]},"references-count":42,"alternative-id":["10.1145\/3377811.3380378","10.1145\/3377811"],"URL":"https:\/\/doi.org\/10.1145\/3377811.3380378","relation":{},"subject":[],"published":{"date-parts":[[2020,6,27]]},"assertion":[{"value":"2020-10-01","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}