{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T12:50:45Z","timestamp":1785243045993,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":62,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T00:00:00Z","timestamp":1653264000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Australian Government's Cooperative Research Centres Programme"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,5,23]]},"DOI":"10.1145\/3524842.3527949","type":"proceedings-article","created":{"date-parts":[[2022,10,18]],"date-time":"2022-10-18T00:08:36Z","timestamp":1666051716000},"page":"596-607","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":217,"title":["LineVD"],"prefix":"10.1145","author":[{"given":"David","family":"Hin","sequence":"first","affiliation":[{"name":"University of Adelaide, Adelaide, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrey","family":"Kan","sequence":"additional","affiliation":[{"name":"AWS AI Labs, Adelaide, SA, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huaming","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Adelaide, Adelaide, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. Ali","family":"Babar","sequence":"additional","affiliation":[{"name":"University of Adelaide, Adelaide, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"2021. NIST: National Vulnerability Database. https:\/\/nvd.nist.gov\/.  2021. NIST: National Vulnerability Database. https:\/\/nvd.nist.gov\/."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.naacl-main.211"},{"key":"e_1_3_2_1_3_1","volume-title":"International Conference on Learning Representations. 1--17","author":"Allamanis Miltiadis","year":"2018","unstructured":"Miltiadis Allamanis , Marc Brockschmidt , and Mahmoud Khademi . 2018 . Learning to Represent Programs with Graphs . In International Conference on Learning Representations. 1--17 . Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi. 2018. Learning to Represent Programs with Graphs. In International Conference on Learning Representations. 1--17."},{"key":"e_1_3_2_1_4_1","volume-title":"International Conference on Learning Representations (ICLR). 1--16","author":"Alon Uri","year":"2020","unstructured":"Uri Alon and Eran Yahav . 2020 . On the bottleneck of graph neural networks and its practical implications . In International Conference on Learning Representations (ICLR). 1--16 . Uri Alon and Eran Yahav. 2020. On the bottleneck of graph neural networks and its practical implications. In International Conference on Learning Representations (ICLR). 1--16."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2019.110427"},{"key":"e_1_3_2_1_6_1","volume-title":"Retrieved","year":"2022","unstructured":"Authors. 2022 . Reproduction package for MSR double-blind review . Retrieved Jan, 2022 from. https:\/\/github.com\/davidhin\/linevd Authors. 2022. Reproduction package for MSR double-blind review. Retrieved Jan, 2022 from. https:\/\/github.com\/davidhin\/linevd"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331273"},{"key":"e_1_3_2_1_8_1","unstructured":"Luca Buratti Saurabh Pujar Mihaela Bornea Scott McCarley Yunhui Zheng Gaetano Rossiello Alessandro Morari Jim Laredo Veronika Thost Yufan Zhuang etal 2020. Exploring software naturalness through neural language models. arXiv preprint arXiv:2006.12641 (2020).  Luca Buratti Saurabh Pujar Mihaela Bornea Scott McCarley Yunhui Zheng Gaetano Rossiello Alessandro Morari Jim Laredo Veronika Thost Yufan Zhuang et al. 2020. Exploring software naturalness through neural language models. arXiv preprint arXiv:2006.12641 (2020)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2021.106576"},{"key":"e_1_3_2_1_10_1","volume-title":"Deep learning based vulnerability detection: Are we there yet","author":"Chakraborty Saikat","year":"2021","unstructured":"Saikat Chakraborty , Rahul Krishna , Yangruibo Ding , and Baishakhi Ray . 2021. Deep learning based vulnerability detection: Are we there yet . IEEE Transactions on Software Engineering ( 2021 ). Saikat Chakraborty, Rahul Krishna, Yangruibo Ding, and Baishakhi Ray. 2021. Deep learning based vulnerability detection: Are we there yet. IEEE Transactions on Software Engineering (2021)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301485"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3436877"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3475716.3475781"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.3047756"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/SANER53432.2022.00114"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00024"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/648"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10664-020-09868-x"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3379597.3387501"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.139"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3092566"},{"key":"e_1_3_2_1_22_1","volume-title":"Mohd Faizal Ab Razak, Ahmad Firdaus, and Nor Badrul Anuar.","author":"Hanif Hazim","year":"2021","unstructured":"Hazim Hanif , Mohd Hairul Nizam Md Nasir , Mohd Faizal Ab Razak, Ahmad Firdaus, and Nor Badrul Anuar. 2021 . The rise of software vulnerability: Taxonomy of software vulnerabilities detection and machine learning approaches. Journal of Network and Computer Applications ( 2021), 103009. Hazim Hanif, Mohd Hairul Nizam Md Nasir, Mohd Faizal Ab Razak, Ahmad Firdaus, and Nor Badrul Anuar. 2021. The rise of software vulnerability: Taxonomy of software vulnerabilities detection and machine learning approaches. Journal of Network and Computer Applications (2021), 103009."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3377811.3380361"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/2961111.2962612"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcss.2014.02.005"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2813885.2737957"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.62"},{"key":"e_1_3_2_1_28_1","volume-title":"Proceedings of the 5th International Conference on Learning Representations (Palais des Congr\u00e8s Neptune","author":"Thomas","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks . In Proceedings of the 5th International Conference on Learning Representations (Palais des Congr\u00e8s Neptune , Toulon, France) (ICLR '17). Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In Proceedings of the 5th International Conference on Learning Representations (Palais des Congr\u00e8s Neptune, Toulon, France) (ICLR '17)."},{"key":"e_1_3_2_1_29_1","volume-title":"International Conference on Machine Learning. PMLR, 1188--1196","author":"Le Quoc","year":"2014","unstructured":"Quoc Le and Tomas Mikolov . 2014 . Distributed representations of sentences and documents . In International Conference on Machine Learning. PMLR, 1188--1196 . Quoc Le and Tomas Mikolov. 2014. Distributed representations of sentences and documents. In International Conference on Machine Learning. PMLR, 1188--1196."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3383458"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2008.35"},{"key":"e_1_3_2_1_32_1","volume-title":"Vulnerability Detection with Finegrained Interpretations. In The 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. ACM.","author":"Li Yi","year":"2021","unstructured":"Yi Li , Shaohua Wang , and Tien N Nguyen . 2021 . Vulnerability Detection with Finegrained Interpretations. In The 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. ACM. Yi Li, Shaohua Wang, and Tien N Nguyen. 2021. Vulnerability Detection with Finegrained Interpretations. In The 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering. ACM."},{"key":"e_1_3_2_1_33_1","volume-title":"Vuldeelocator: a deep learning-based fine-grained vulnerability detector","author":"Li Zhen","year":"2021","unstructured":"Zhen Li , Deqing Zou , Shouhuai Xu , Zhaoxuan Chen , Yawei Zhu , and Hai Jin . 2021. Vuldeelocator: a deep learning-based fine-grained vulnerability detector . IEEE Transactions on Dependable and Secure Computing ( 2021 ), 1--17. Zhen Li, Deqing Zou, Shouhuai Xu, Zhaoxuan Chen, Yawei Zhu, and Hai Jin. 2021. Vuldeelocator: a deep learning-based fine-grained vulnerability detector. IEEE Transactions on Dependable and Secure Computing (2021), 1--17."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/2991079.2991102"},{"key":"e_1_3_2_1_35_1","volume-title":"SySeVR: A framework for using deep learning to detect software vulnerabilities","author":"Li Zhen","year":"2021","unstructured":"Zhen Li , Deqing Zou , Shouhuai Xu , Hai Jin , Yawei Zhu , and Zhaoxuan Chen . 2021. SySeVR: A framework for using deep learning to detect software vulnerabilities . IEEE Transactions on Dependable and Secure Computing ( 2021 ). Zhen Li, Deqing Zou, Shouhuai Xu, Hai Jin, Yawei Zhu, and Zhaoxuan Chen. 2021. SySeVR: A framework for using deep learning to detect software vulnerabilities. IEEE Transactions on Dependable and Secure Computing (2021)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2018.23158"},{"key":"e_1_3_2_1_37_1","volume-title":"Tune: A research platform for distributed model selection and training. arXiv preprint arXiv:1807.05118","author":"Liaw Richard","year":"2018","unstructured":"Richard Liaw , Eric Liang , Robert Nishihara , Philipp Moritz , Joseph E Gonzalez , and Ion Stoica . 2018 . Tune: A research platform for distributed model selection and training. arXiv preprint arXiv:1807.05118 (2018). Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E Gonzalez, and Ion Stoica. 2018. Tune: A research platform for distributed model selection and training. arXiv preprint arXiv:1807.05118 (2018)."},{"key":"e_1_3_2_1_38_1","volume-title":"Shang Gao, Yonghang Tai, and Jun Zhang.","author":"Lin Guanjun","year":"2021","unstructured":"Guanjun Lin , Wei Xiao , Leo Yu Zhang , Shang Gao, Yonghang Tai, and Jun Zhang. 2021 . Deep neural-based vulnerability discovery demystified: data, model and performance. Neural Computing and Applications ( 2021), 1--14. Guanjun Lin, Wei Xiao, Leo Yu Zhang, Shang Gao, Yonghang Tai, and Jun Zhang. 2021. Deep neural-based vulnerability discovery demystified: data, model and performance. Neural Computing and Applications (2021), 1--14."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/MINES.2012.202"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150522"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3324884.3416591"},{"key":"e_1_3_2_1_42_1","volume-title":"Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692","author":"Liu Yinhan","year":"2019","unstructured":"Yinhan Liu , Myle Ott , Naman Goyal , Jingfei Du , Mandar Joshi , Danqi Chen , Omer Levy , Mike Lewis , Luke Zettlemoyer , and Veselin Stoyanov . 2019 . Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019). Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)."},{"key":"e_1_3_2_1_43_1","unstructured":"Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems. 3111--3119.  Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems. 3111--3119."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/SANER48275.2020.9054817"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.5555\/3104322.3104425"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"crossref","unstructured":"Hai Ngoc Nguyen Songpon Teerakanok Atsuo Inomata and Tetsutaro Uehara. 2021. The Comparison of Word Embedding Techniques in RNNs for Vulnerability Detection.. In ICISSP. 109--120.  Hai Ngoc Nguyen Songpon Teerakanok Atsuo Inomata and Tetsutaro Uehara. 2021. The Comparison of Word Embedding Techniques in RNNs for Vulnerability Detection.. In ICISSP. 109--120.","DOI":"10.5220\/0010232301090120"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/1858996.1859089"},{"key":"e_1_3_2_1_49_1","volume-title":"Finer-grained Just-In-Time Defect Prediction. In 2021 International Conference on Mining Software Repositories (MSR'21)","author":"Pornprasit Chanathip","year":"2021","unstructured":"Chanathip Pornprasit and Chakkrit Tantithamthavorn . 2021 . JITLine: A Simpler, Better, Faster , Finer-grained Just-In-Time Defect Prediction. In 2021 International Conference on Mining Software Repositories (MSR'21) . 1--11. Chanathip Pornprasit and Chakkrit Tantithamthavorn. 2021. JITLine: A Simpler, Better, Faster, Finer-grained Just-In-Time Defect Prediction. In 2021 International Conference on Mining Software Repositories (MSR'21). 1--11."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2022.3144348"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/2884781.2884877"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2014.2340398"},{"key":"e_1_3_2_1_53_1","volume-title":"Markus Hagenbuchner, and Gabriele Monfardini.","author":"Scarselli Franco","year":"2008","unstructured":"Franco Scarselli , Marco Gori , Ah Chung Tsoi , Markus Hagenbuchner, and Gabriele Monfardini. 2008 . The graph neural network model. IEEE transactions on neural networks 20, 1 (2008), 61--80. Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2008. The graph neural network model. IEEE transactions on neural networks 20, 1 (2008), 61--80."},{"key":"e_1_3_2_1_54_1","unstructured":"Rico Sennrich Barry Haddow and Alexandra Birch. 2016. Neural Machine Translation of Rare Words with Subword Units. (2016) 1715--1725.  Rico Sennrich Barry Haddow and Alexandra Birch. 2016. Neural Machine Translation of Rare Words with Subword Units. (2016) 1715--1725."},{"key":"e_1_3_2_1_55_1","volume-title":"Proceedings of the 6th International Conference on Learning Representations. 1--12","author":"Veli\u010dkovi\u0107 Petar","year":"2018","unstructured":"Petar Veli\u010dkovi\u0107 , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Li\u00f2 , and Yoshua Bengio . 2018 . Graph Attention Networks . In Proceedings of the 6th International Conference on Learning Representations. 1--12 . Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph Attention Networks. In Proceedings of the 6th International Conference on Learning Representations. 1--12."},{"key":"e_1_3_2_1_56_1","volume-title":"Mul-Code: A Multi-task Learning Approach for Source Code Understanding. In 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 48--59","author":"Wang Deze","year":"2021","unstructured":"Deze Wang , Yue Yu , Shanshan Li , Wei Dong , Ji Wang , and Liao Qing . 2021 . Mul-Code: A Multi-task Learning Approach for Source Code Understanding. In 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 48--59 . Deze Wang, Yue Yu, Shanshan Li, Wei Dong, Ji Wang, and Liao Qing. 2021. Mul-Code: A Multi-task Learning Approach for Source Code Understanding. In 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER). IEEE, 48--59."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/SANER48275.2020.9054857"},{"key":"e_1_3_2_1_58_1","first-page":"1","article-title":"Predicting Defective Lines Using a Model-Agnostic Technique","volume":"01","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 01 (2020), 1 -- 1 . 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 01 (2020), 1--1.","journal-title":"IEEE Transactions on Software Engineering"},{"key":"e_1_3_2_1_59_1","volume-title":"Breakthroughs in statistics","author":"Wilcoxon Frank","unstructured":"Frank Wilcoxon . 1992. Individual comparisons by ranking methods . In Breakthroughs in statistics . Springer , 196--202. Frank Wilcoxon. 1992. Individual comparisons by ranking methods. In Breakthroughs in statistics. Springer, 196--202."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2014.44"},{"key":"e_1_3_2_1_61_1","first-page":"8026","article-title":"Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks","volume":"32","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 32 (2019), 8026 -- 8037 . 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 32 (2019), 8026--8037.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_62_1","first-page":"1","article-title":"Interpreting deep learning-based vulnerability detector predictions based on heuristic searching","volume":"30","author":"Zou Deqing","year":"2021","unstructured":"Deqing Zou , Yawei Zhu , Shouhuai Xu , Zhen Li , Hai Jin , and Hengkai Ye . 2021 . Interpreting deep learning-based vulnerability detector predictions based on heuristic searching . ACM Transactions on Software Engineering and Methodology (TOSEM) 30 , 2 (2021), 1 -- 31 . Deqing Zou, Yawei Zhu, Shouhuai Xu, Zhen Li, Hai Jin, and Hengkai Ye. 2021. Interpreting deep learning-based vulnerability detector predictions based on heuristic searching. ACM Transactions on Software Engineering and Methodology (TOSEM) 30, 2 (2021), 1--31.","journal-title":"ACM Transactions on Software Engineering and Methodology (TOSEM)"}],"event":{"name":"MSR '22: 19th International Conference on Mining Software Repositories","location":"Pittsburgh Pennsylvania","acronym":"MSR '22","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering","IEEE CS"]},"container-title":["Proceedings of the 19th International Conference on Mining Software Repositories"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3524842.3527949","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3524842.3527949","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:54Z","timestamp":1750183794000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3524842.3527949"}},"subtitle":["statement-level vulnerability detection using graph neural networks"],"short-title":[],"issued":{"date-parts":[[2022,5,23]]},"references-count":62,"alternative-id":["10.1145\/3524842.3527949","10.1145\/3524842"],"URL":"https:\/\/doi.org\/10.1145\/3524842.3527949","relation":{},"subject":[],"published":{"date-parts":[[2022,5,23]]},"assertion":[{"value":"2022-10-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}