{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T20:57:38Z","timestamp":1774904258761,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":65,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T00:00:00Z","timestamp":1667779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DE200100941"],"award-info":[{"award-number":["DE200100941"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,11,7]]},"DOI":"10.1145\/3540250.3549098","type":"proceedings-article","created":{"date-parts":[[2022,11,9]],"date-time":"2022-11-09T20:46:22Z","timestamp":1668026782000},"page":"935-947","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":169,"title":["VulRepair: a T5-based automated software vulnerability repair"],"prefix":"10.1145","author":[{"given":"Michael","family":"Fu","sequence":"first","affiliation":[{"name":"Monash University, Australia"}]},{"given":"Chakkrit","family":"Tantithamthavorn","sequence":"additional","affiliation":[{"name":"Monash University, Australia"}]},{"given":"Trung","family":"Le","sequence":"additional","affiliation":[{"name":"Monash University, Australia"}]},{"given":"Van","family":"Nguyen","sequence":"additional","affiliation":[{"name":"University of Adelaide, Australia"}]},{"given":"Dinh","family":"Phung","sequence":"additional","affiliation":[{"name":"Monash University, Australia"}]}],"member":"320","published-online":{"date-parts":[[2022,11,9]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"[n.d.]. 25+ cyber security vulnerability statistics and facts of 2021. https:\/\/www.comparitech.com\/blog\/information-security\/cybersecurity-vulnerability-statistics\/ \t\t\t\t  [n.d.]. 25+ cyber security vulnerability statistics and facts of 2021. https:\/\/www.comparitech.com\/blog\/information-security\/cybersecurity-vulnerability-statistics\/"},{"key":"e_1_3_2_1_2_1","unstructured":"[n.d.]. Checkmarx. https:\/\/checkmarx.com\/ \t\t\t\t  [n.d.]. Checkmarx. https:\/\/checkmarx.com\/"},{"key":"e_1_3_2_1_3_1","unstructured":"[n.d.]. Cppcheck. https:\/\/cppcheck.sourceforge.io\/ \t\t\t\t  [n.d.]. Cppcheck. https:\/\/cppcheck.sourceforge.io\/"},{"key":"e_1_3_2_1_4_1","unstructured":"[n.d.]. Fixing Vulnerabilities Costs 100x More If You Don\u2019t Understand the Weakness. https:\/\/medium.com\/@CWE_CAPEC\/fixing-vulnerabilities-costs-100x-more-if-you-dont-understand-the-weakness-c3877f68d8a6 \t\t\t\t  [n.d.]. Fixing Vulnerabilities Costs 100x More If You Don\u2019t Understand the Weakness. https:\/\/medium.com\/@CWE_CAPEC\/fixing-vulnerabilities-costs-100x-more-if-you-dont-understand-the-weakness-c3877f68d8a6"},{"key":"e_1_3_2_1_5_1","unstructured":"[n.d.]. Flawfinder. https:\/\/dwheeler.com\/flawfinder\/ \t\t\t\t  [n.d.]. Flawfinder. https:\/\/dwheeler.com\/flawfinder\/"},{"key":"e_1_3_2_1_6_1","unstructured":"[n.d.]. National Vulnerability Database. https:\/\/nvd.nist.gov\/ \t\t\t\t  [n.d.]. National Vulnerability Database. https:\/\/nvd.nist.gov\/"},{"key":"e_1_3_2_1_7_1","unstructured":"[n.d.]. RATS. https:\/\/code.google.com\/archive\/p\/rough-auditing-tool-for-security\/ \t\t\t\t  [n.d.]. RATS. https:\/\/code.google.com\/archive\/p\/rough-auditing-tool-for-security\/"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.7080271"},{"key":"e_1_3_2_1_9_1","volume-title":"Jamie Ryan Kiros, and Geoffrey E Hinton","author":"Ba Jimmy Lei","year":"2016","unstructured":"Jimmy Lei Ba , Jamie Ryan Kiros, and Geoffrey E Hinton . 2016 . Layer normalization. arXiv preprint arXiv:1607.06450. Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016. Layer normalization. arXiv preprint arXiv:1607.06450."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3475960.3475985"},{"key":"e_1_3_2_1_11_1","unstructured":"Tom Britton Lisa Jeng Graham Carver and Paul Cheak. 2013. Reversible Debugging Software \u201cQuantify the time and cost saved using reversible debuggers\u201d. \t\t\t\t  Tom Britton Lisa Jeng Graham Carver and Paul Cheak. 2013. Reversible Debugging Software \u201cQuantify the time and cost saved using reversible debuggers\u201d."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2021.3087402"},{"key":"e_1_3_2_1_13_1","article-title":"Neural Transfer Learning for Repairing Security Vulnerabilities in C Code","author":"Chen Zimin","year":"2021","unstructured":"Zimin Chen , Steve Kommrusch , and Martin Monperrus . 2021 . Neural Transfer Learning for Repairing Security Vulnerabilities in C Code . IEEE Transactions on Software Engineering. Zimin Chen, Steve Kommrusch, and Martin Monperrus. 2021. Neural Transfer Learning for Repairing Security Vulnerabilities in C Code. IEEE Transactions on Software Engineering.","journal-title":"IEEE Transactions on Software Engineering."},{"key":"e_1_3_2_1_14_1","first-page":"1943","article-title":"Sequencer: Sequence-to-sequence learning for end-to-end program repair","volume":"47","author":"Chen Zimin","year":"2019","unstructured":"Zimin Chen , Steve Kommrusch , Michele Tufano , Louis-No\u00ebl Pouchet , Denys Poshyvanyk , and Martin Monperrus . 2019 . Sequencer: Sequence-to-sequence learning for end-to-end program repair . IEEE Transactions on Software Engineering , 47 , 9 (2019), 1943 \u2013 1959 . Zimin Chen, Steve Kommrusch, Michele Tufano, Louis-No\u00ebl Pouchet, Denys Poshyvanyk, and Martin Monperrus. 2019. Sequencer: Sequence-to-sequence learning for end-to-end program repair. IEEE Transactions on Software Engineering, 47, 9 (2019), 1943\u20131959.","journal-title":"IEEE Transactions on Software Engineering"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2022.3156637"},{"key":"e_1_3_2_1_16_1","volume-title":"NIPS Workshop.","author":"Collobert R.","unstructured":"R. Collobert , K. Kavukcuoglu , and C. Farabet . 2011. Torch7: A Matlab-like Environment for Machine Learning. In BigLearn , NIPS Workshop. R. Collobert, K. Kavukcuoglu, and C. Farabet. 2011. Torch7: A Matlab-like Environment for Machine Learning. In BigLearn, NIPS Workshop."},{"key":"e_1_3_2_1_17_1","unstructured":"M. Dowd J. McDonald and J. Schuh. 2006. The Art of Software Security Assessment: Identifying and Preventing Software Vulnerabilities. Addison-Wesley Professional. isbn:0321444426 \t\t\t\t  M. Dowd J. McDonald and J. Schuh. 2006. The Art of Software Security Assessment: Identifying and Preventing Software Vulnerabilities. Addison-Wesley Professional. isbn:0321444426"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3379597.3387501"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.139"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3524842.3528452"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3524842.3527949"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2902362"},{"key":"e_1_3_2_1_23_1","unstructured":"Hamel Husain Ho-Hsiang Wu Tiferet Gazit Miltiadis Allamanis and Marc Brockschmidt. 2019. Codesearchnet challenge: Evaluating the state of semantic code search. arXiv preprint arXiv:1909.09436. \t\t\t\t  Hamel Husain Ho-Hsiang Wu Tiferet Gazit Miltiadis Allamanis and Marc Brockschmidt. 2019. Codesearchnet challenge: Evaluating the state of semantic code search. arXiv preprint arXiv:1909.09436."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE43902.2021.00107"},{"key":"e_1_3_2_1_25_1","article-title":"An Empirical Study of Model-Agnostic Techniques for Defect Prediction Models","author":"Jiarpakdee Jirayus","year":"2020","unstructured":"Jirayus Jiarpakdee , Chakkrit Tantithamthavorn , Hoa Khanh Dam , and John Grundy . 2020 . An Empirical Study of Model-Agnostic Techniques for Defect Prediction Models . IEEE Transactions on Software Engineering (TSE), To Appear. Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Hoa Khanh Dam, and John Grundy. 2020. An Empirical Study of Model-Agnostic Techniques for Defect Prediction Models. IEEE Transactions on Software Engineering (TSE), To Appear.","journal-title":"IEEE Transactions on Software Engineering (TSE), To Appear."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSR52588.2021.00055"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377811.3380342"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3324884.3415295"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377811.3380345"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2020.110817"},{"key":"e_1_3_2_1_31_1","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR).","author":"Loshchilov Ilya","year":"2017","unstructured":"Ilya Loshchilov and Frank Hutter . 2017 . Decoupled weight decay regularization . in Proceedings of the International Conference on Learning Representations (ICLR). Ilya Loshchilov and Frank Hutter. 2017. Decoupled weight decay regularization. in Proceedings of the International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3395363.3397369"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSR52588.2021.00063"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-017-9541-1"},{"key":"e_1_3_2_1_35_1","volume-title":"27th USENIX Security Symposium (USENIX Security 18)","author":"Mu Dongliang","year":"2018","unstructured":"Dongliang Mu , Alejandro Cuevas , Limin Yang , Hang Hu , Xinyu Xing , Bing Mao , and Gang Wang . 2018 . Understanding the reproducibility of crowd-reported security vulnerabilities . In 27th USENIX Security Symposium (USENIX Security 18) . 919\u2013936. Dongliang Mu, Alejandro Cuevas, Limin Yang, Hang Hu, Xinyu Xing, Bing Mao, and Gang Wang. 2018. Understanding the reproducibility of crowd-reported security vulnerabilities. In 27th USENIX Security Symposium (USENIX Security 18). 919\u2013936."},{"key":"e_1_3_2_1_36_1","volume-title":"Information-theoretic Source Code Vulnerability Highlighting. In International Joint Conference on Neural Networks (IJCNN).","author":"Nguyen Van","year":"2021","unstructured":"Van Nguyen , Trung Le , Olivier de Vel , Paul Montague , John Grundy , and Dinh Phung . 2021 . Information-theoretic Source Code Vulnerability Highlighting. In International Joint Conference on Neural Networks (IJCNN). Van Nguyen, Trung Le, Olivier de Vel, Paul Montague, John Grundy, and Dinh Phung. 2021. Information-theoretic Source Code Vulnerability Highlighting. In International Joint Conference on Neural Networks (IJCNN)."},{"key":"e_1_3_2_1_37_1","volume-title":"Deep Domain Adaptation for Vulnerable Code Function Identification. In The International Joint Conference on Neural Networks (IJCNN).","author":"Nguyen V.","unstructured":"V. Nguyen , T. Le , T. Le , K. Nguyen , O. DeVel , P. Montague , L. Qu , and D. Phung . 2019 . Deep Domain Adaptation for Vulnerable Code Function Identification. In The International Joint Conference on Neural Networks (IJCNN). V. Nguyen, T. Le, T. Le, K. Nguyen, O. DeVel, P. Montague, L. Qu, and D. Phung. 2019. Deep Domain Adaptation for Vulnerable Code Function Identification. In The International Joint Conference on Neural Networks (IJCNN)."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSR52588.2021.00049"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2022.3144348"},{"key":"e_1_3_2_1_40_1","volume-title":"PyExplainer: Explaining the Predictions of Just-In-Time Defect Models. In 2021 36th IEEE\/ACM International Conference on Automated Software Engineering (ASE). 407\u2013418","author":"Pornprasit Chanathip","year":"2021","unstructured":"Chanathip Pornprasit , Chakkrit Tantithamthavorn , Jirayus Jiarpakdee , Michael Fu , and Patanamon Thongtanunam . 2021 . PyExplainer: Explaining the Predictions of Just-In-Time Defect Models. In 2021 36th IEEE\/ACM International Conference on Automated Software Engineering (ASE). 407\u2013418 . Chanathip Pornprasit, Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, Michael Fu, and Patanamon Thongtanunam. 2021. PyExplainer: Explaining the Predictions of Just-In-Time Defect Models. In 2021 36th IEEE\/ACM International Conference on Automated Software Engineering (ASE). 407\u2013418."},{"key":"e_1_3_2_1_41_1","unstructured":"Alec Radford Karthik Narasimhan Tim Salimans and Ilya Sutskever. 2018. Improving language understanding by generative pre-training. \t\t\t\t  Alec Radford Karthik Narasimhan Tim Salimans and Ilya Sutskever. 2018. Improving language understanding by generative pre-training."},{"key":"e_1_3_2_1_42_1","article-title":"Exploring the limits of transfer learning with a unified text-to-text transformer","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 . Journal of Machine Learning Research (JMLR). 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. Journal of Machine Learning Research (JMLR).","journal-title":"Journal of Machine Learning Research (JMLR)."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2018.00120"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-17465-1_22"},{"key":"e_1_3_2_1_45_1","volume-title":"Proceedings of the Association for Computational Linguistics (ACL).","author":"Sennrich Rico","year":"2015","unstructured":"Rico Sennrich , Barry Haddow , and Alexandra Birch . 2015 . Neural machine translation of rare words with subword units . in Proceedings of the Association for Computational Linguistics (ACL). Rico Sennrich, Barry Haddow, and Alexandra Birch. 2015. Neural machine translation of rare words with subword units. in Proceedings of the Association for Computational Linguistics (ACL)."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-2074"},{"key":"e_1_3_2_1_47_1","volume-title":"Sequence to sequence learning with neural networks. Advances in neural information processing systems (NeurIPS), 27","author":"Sutskever Ilya","year":"2014","unstructured":"Ilya Sutskever , Oriol Vinyals , and Quoc V Le. 2014. Sequence to sequence learning with neural networks. Advances in neural information processing systems (NeurIPS), 27 ( 2014 ). Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural networks. Advances in neural information processing systems (NeurIPS), 27 (2014)."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/MS.2021.3072088"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/2884781.2884857"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2016.2584050"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2018.2794977"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510067"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00021"},{"key":"e_1_3_2_1_54_1","volume-title":"\u0141 ukasz Kaiser, and Illia Polosukhin","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani , Noam Shazeer , Niki Parmar , Jakob Uszkoreit , Llion Jones , Aidan N Gomez , \u0141 ukasz Kaiser, and Illia Polosukhin . 2017 . Attention is all you need. Advances in neural information processing systems (NeurIPS) , 30 (2017). Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141 ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems (NeurIPS), 30 (2017)."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.3044773"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.685"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1145\/1250734.1250739"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/1368088.1368112"},{"key":"e_1_3_2_1_59_1","article-title":"Predicting defective lines using a model-agnostic technique","author":"Wattanakriengkrai Supatsara","year":"2020","unstructured":"Supatsara Wattanakriengkrai , Patanamon Thongtanunam , Chakkrit Tantithamthavorn , Hideaki Hata , and Kenichi Matsumoto . 2020 . Predicting defective lines using a model-agnostic technique . IEEE Transactions on Software Engineering (TSE). Supatsara Wattanakriengkrai, Patanamon Thongtanunam, Chakkrit Tantithamthavorn, Hideaki Hata, and Kenichi Matsumoto. 2020. Predicting defective lines using a model-agnostic technique. IEEE Transactions on Software Engineering (TSE).","journal-title":"IEEE Transactions on Software Engineering (TSE)."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"crossref","unstructured":"Thomas Wolf Lysandre Debut Victor Sanh Julien Chaumond Clement Delangue Anthony Moi Pierric Cistac Tim Rault R\u00e9mi Louf and Morgan Funtowicz. 2019. Huggingface\u2019s transformers: State-of-the-art natural language processing. arXiv preprint arXiv:1910.03771. \t\t\t\t  Thomas Wolf Lysandre Debut Victor Sanh Julien Chaumond Clement Delangue Anthony Moi Pierric Cistac Tim Rault R\u00e9mi Louf and Morgan Funtowicz. 2019. Huggingface\u2019s transformers: State-of-the-art natural language processing. arXiv preprint arXiv:1910.03771.","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"e_1_3_2_1_61_1","unstructured":"Yonghui Wu Mike Schuster Zhifeng Chen Quoc V Le Mohammad Norouzi Wolfgang Macherey Maxim Krikun Yuan Cao Qin Gao and Klaus Macherey. 2016. Google\u2019s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144. \t\t\t\t  Yonghui Wu Mike Schuster Zhifeng Chen Quoc V Le Mohammad Norouzi Wolfgang Macherey Maxim Krikun Yuan Cao Qin Gao and Klaus Macherey. 2016. Google\u2019s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144."},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3180155.3180178"},{"key":"e_1_3_2_1_63_1","article-title":"Improving vulnerability inspection efficiency using active learning","author":"Yu Zhe","year":"2019","unstructured":"Zhe Yu , Christopher Theisen , Laurie Williams , and Tim Menzies . 2019 . Improving vulnerability inspection efficiency using active learning . IEEE Transactions on Software Engineering. Zhe Yu, Christopher Theisen, Laurie Williams, and Tim Menzies. 2019. Improving vulnerability inspection efficiency using active learning. IEEE Transactions on Software Engineering.","journal-title":"IEEE Transactions on Software Engineering."},{"key":"e_1_3_2_1_64_1","unstructured":"Wenyuan Zeng Wenjie Luo Sanja Fidler and Raquel Urtasun. 2016. Efficient summarization with read-again and copy mechanism. arXiv preprint arXiv:1611.03382. \t\t\t\t  Wenyuan Zeng Wenjie Luo Sanja Fidler and Raquel Urtasun. 2016. Efficient summarization with read-again and copy mechanism. arXiv preprint arXiv:1611.03382."},{"key":"e_1_3_2_1_65_1","volume-title":"Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks. Advances in neural information processing systems (NeurIPS).","author":"Zhou Yaqin","year":"2019","unstructured":"Yaqin Zhou , Shangqing Liu , Jingkai Siow , Xiaoning Du , and Yang Liu . 2019 . Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks. Advances in neural information processing systems (NeurIPS). Yaqin Zhou, Shangqing Liu, Jingkai Siow, Xiaoning Du, and Yang Liu. 2019. Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks. Advances in neural information processing systems (NeurIPS)."}],"event":{"name":"ESEC\/FSE '22: 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering","location":"Singapore Singapore","acronym":"ESEC\/FSE '22","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering","NUS NUS"]},"container-title":["Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3540250.3549098","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3540250.3549098","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:51:02Z","timestamp":1750182662000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3540250.3549098"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,7]]},"references-count":65,"alternative-id":["10.1145\/3540250.3549098","10.1145\/3540250"],"URL":"https:\/\/doi.org\/10.1145\/3540250.3549098","relation":{},"subject":[],"published":{"date-parts":[[2022,11,7]]},"assertion":[{"value":"2022-11-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}