{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T21:16:08Z","timestamp":1783804568931,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":63,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T00:00:00Z","timestamp":1672876800000},"content-version":"vor","delay-in-days":87,"URL":"http:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["CCF-1652517, CCF-2107291, IIS-2107524, IIS-2145479"],"award-info":[{"award-number":["CCF-1652517, CCF-2107291, IIS-2107524, IIS-2145479"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,10]]},"DOI":"10.1145\/3551349.3556955","type":"proceedings-article","created":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T20:43:54Z","timestamp":1672951434000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":74,"title":["CoditT5: Pretraining for Source Code and Natural Language Editing"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8211-3321","authenticated-orcid":false,"given":"Jiyang","family":"Zhang","sequence":"first","affiliation":[{"name":"The University of Texas at Austin, United States of America"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheena","family":"Panthaplackel","sequence":"additional","affiliation":[{"name":"The University of Texas at Austin, United States of America"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengyu","family":"Nie","sequence":"additional","affiliation":[{"name":"The University of Texas at Austin, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junyi Jessy","family":"Li","sequence":"additional","affiliation":[{"name":"The University of Texas at Austin, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Milos","family":"Gligoric","sequence":"additional","affiliation":[{"name":"The University of Texas at Austin, United States of America"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,1,5]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Unified Pre-training for Program Understanding and Generation. In Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2655\u20132668","author":"Ahmad Wasi","year":"2021","unstructured":"Wasi Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2021. Unified Pre-training for Program Understanding and Generation. In Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2655\u20132668."},{"key":"e_1_3_2_1_2_1","volume-title":"International Conference on Learning Representations.","author":"Bahdanau Dzmitry","year":"2015","unstructured":"Dzmitry Bahdanau, Kyung\u00a0Hyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_3_1","volume-title":"ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and\/or Summarization. 65\u201372","author":"Banerjee Satanjeev","year":"2005","unstructured":"Satanjeev Banerjee and Alon Lavie. 2005. METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and\/or Summarization. 65\u201372."},{"key":"e_1_3_2_1_4_1","volume-title":"An Empirical Investigation of Statistical Significance in NLP. In Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 995\u20131005","author":"Berg-Kirkpatrick Taylor","year":"2012","unstructured":"Taylor Berg-Kirkpatrick, David Burkett, and Dan Klein. 2012. An Empirical Investigation of Statistical Significance in NLP. In Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. 995\u20131005."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Avrim Blum and Tom Mitchell. 1998. Combining labeled and unlabeled data with co-training. In Computational Learning Theory. 92\u2013100.","DOI":"10.1145\/279943.279962"},{"key":"e_1_3_2_1_6_1","first-page":"1","article-title":"A structural model for contextual code changes. International Conference on Object-Oriented Programming","volume":"4","author":"Brody Shaked","year":"2020","unstructured":"Shaked Brody, Uri Alon, and Eran Yahav. 2020. A structural model for contextual code changes. International Conference on Object-Oriented Programming, Systems, Languages, and Applications 4(2020), 1\u201328.","journal-title":"Systems, Languages, and Applications"},{"key":"e_1_3_2_1_7_1","unstructured":"Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared\u00a0D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell 2020. Language models are few-shot learners. In Advances in Neural Information Processing Systems. 1877\u20131901."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Saikat Chakraborty and Baishakhi Ray. 2021. On Multi-Modal Learning of Editing Source Code. In Automated Software Engineering. 443\u2013455.","DOI":"10.1109\/ASE51524.2021.9678559"},{"key":"e_1_3_2_1_9_1","volume-title":"Jared Kaplan, Harri Edwards, Yuri Burda","author":"Chen Mark","year":"2021","unstructured":"Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de\u00a0Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, 2021. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374(2021)."},{"key":"e_1_3_2_1_10_1","volume-title":"PLUR: A unifying, graph-based view of program learning, understanding, and repair. In Advances in Neural Information Processing Systems. 23089\u201323101.","author":"Chen Zimin","year":"2021","unstructured":"Zimin Chen, Vincent\u00a0J Hellendoorn, Pascal Lamblin, Petros Maniatis, Pierre-Antoine Manzagol, Daniel Tarlow, and Subhodeep Moitra. 2021. PLUR: A unifying, graph-based view of program learning, understanding, and repair. In Advances in Neural Information Processing Systems. 23089\u201323101."},{"key":"e_1_3_2_1_11_1","unstructured":"Kyunghyun Cho Bart van Merri\u00ebnboer \u00c7a\u011flar Gul\u00e7ehre Dzmitry Bahdanau Fethi Bougares Holger Schwenk and Yoshua Bengio. 2014. Learning Phrase Representations using RNN Encoder\u2013Decoder for Statistical Machine Translation. In Empirical Methods in Natural Language Processing. 1724\u20131734."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Colin Clement Dawn Drain Jonathan Timcheck Alexey Svyatkovskiy and Neel Sundaresan. 2020. PyMT5: multi-mode translation of natural language and Python code with transformers. In Empirical Methods in Natural Language Processing. 9052\u20139065.","DOI":"10.18653\/v1\/2020.emnlp-main.728"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Yangruibo Ding Baishakhi Ray Premkumar Devanbu and Vincent\u00a0J Hellendoorn. 2020. Patching as Translation: the Data and the Metaphor. In Automated Software Engineering. 275\u2013286.","DOI":"10.1145\/3324884.3416587"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3460945.3464951"},{"key":"e_1_3_2_1_15_1","volume-title":"Strategies for Structuring Story Generation. In Annual Meeting of the Association for Computational Linguistics. 2650\u20132660","author":"Fan Angela","year":"2019","unstructured":"Angela Fan, Mike Lewis, and Yann Dauphin. 2019. Strategies for Structuring Story Generation. In Annual Meeting of the Association for Computational Linguistics. 2650\u20132660."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Zhangyin Feng Daya Guo Duyu Tang Nan Duan Xiaocheng Feng Ming Gong Linjun Shou Bing Qin Ting Liu Daxin Jiang 2020. CodeBERT: A Pre-Trained Model for Programming and Natural Languages. In Empirical Methods in Natural Language Processing: Findings. 1536\u20131547.","DOI":"10.18653\/v1\/2020.findings-emnlp.139"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3468264.3468553"},{"key":"e_1_3_2_1_18_1","volume-title":"Incorporating Copying Mechanism in Sequence-to-Sequence Learning. In Annual Meeting of the Association for Computational Linguistics. 1631\u20131640","author":"Gu Jiatao","year":"2016","unstructured":"Jiatao Gu, Zhengdong Lu, Hang Li, and Victor\u00a0OK Li. 2016. Incorporating Copying Mechanism in Sequence-to-Sequence Learning. In Annual Meeting of the Association for Computational Linguistics. 1631\u20131640."},{"key":"e_1_3_2_1_19_1","volume-title":"UniXcoder: Unified Cross-Modal Pre-training for Code Representation. In Annual Meeting of the Association for Computational Linguistics. 7212\u20137225","author":"Guo Daya","year":"2022","unstructured":"Daya Guo, Shuai Lu, Nan Duan, Yanlin Wang, Ming Zhou, and Jian Yin. 2022. UniXcoder: Unified Cross-Modal Pre-training for Code Representation. In Annual Meeting of the Association for Computational Linguistics. 7212\u20137225."},{"key":"e_1_3_2_1_20_1","volume-title":"GraphCodeBERT: Pre-training Code Representations with Data Flow. In International Conference on Learning Representations.","author":"Guo Daya","year":"2020","unstructured":"Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, LIU Shujie, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, 2020. GraphCodeBERT: Pre-training Code Representations with Data Flow. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00030"},{"key":"e_1_3_2_1_22_1","unstructured":"Tatsunori\u00a0B Hashimoto Kelvin Guu Yonatan Oren and Percy\u00a0S Liang. 2018. A retrieve-and-edit framework for predicting structured outputs. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_23_1","volume-title":"Long Short-Term Memory. Neural computation 9","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter, and J\u00fcrgen Schmidhuber. 1997. Long Short-Term Memory. Neural computation 9 (1997), 1735\u20131780."},{"key":"e_1_3_2_1_24_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(2019)."},{"key":"e_1_3_2_1_25_1","volume-title":"FRUIT: Faithfully Reflecting Updated Information in Text. In Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 3670\u20133686","author":"Iv Robert","year":"2022","unstructured":"Robert Iv, Alexandre Passos, Sameer Singh, and Ming-Wei Chang. 2022. FRUIT: Faithfully Reflecting Updated Information in Text. In Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 3670\u20133686."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"Reno Kriz Jo\u00e3o Sedoc Marianna Apidianaki Carolina Zheng Gaurav Kumar Eleni Miltsakaki and Chris Callison-Burch. 2019. Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification. In Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 3137\u20133147.","DOI":"10.18653\/v1\/N19-1317"},{"key":"e_1_3_2_1_27_1","volume-title":"Ensemble Models for Neural Source Code Summarization of Subroutines. In International Conference on Software Maintenance and Evolution. 286\u2013297","author":"LeClair Alexander","year":"2021","unstructured":"Alexander LeClair, Aakash Bansal, and Collin McMillan. 2021. Ensemble Models for Neural Source Code Summarization of Subroutines. In International Conference on Software Maintenance and Evolution. 286\u2013297."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"e_1_3_2_1_29_1","volume-title":"Editsum: A retrieve-and-edit framework for source code summarization. In Automated Software Engineering. 155\u2013166.","author":"Li Jia","year":"2021","unstructured":"Jia Li, Yongmin Li, Ge Li, Xing Hu, Xin Xia, and Zhi Jin. 2021. Editsum: A retrieve-and-edit framework for source code summarization. In Automated Software Engineering. 155\u2013166."},{"key":"e_1_3_2_1_30_1","unstructured":"Zhiyu Li Shuai Lu Daya Guo Nan Duan Shailesh Jannu Grant Jenks Deep Majumder Jared Green Alexey Svyatkovskiy Shengyu Fu 2022. CodeReviewer: Pre-Training for Automating Code Review Activities. arXiv preprint arXiv:2203.09095(2022)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPC52881.2021.00013"},{"key":"e_1_3_2_1_32_1","volume-title":"Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692(2019).","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)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSE.2021.3138909"},{"key":"e_1_3_2_1_34_1","unstructured":"Shuai Lu Daya Guo Shuo Ren Junjie Huang Alexey Svyatkovskiy Ambrosio Blanco Colin Clement Dawn Drain Daxin Jiang Duyu Tang 2021. CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation. arXiv preprint arXiv:2102.04664(2021)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11430"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE43902.2021.00041"},{"key":"e_1_3_2_1_37_1","volume-title":"Annual Meeting of the Association for Computational Linguistics and International Joint Conference on Natural Language Processing. 588\u2013593","author":"Napoles Courtney","year":"2015","unstructured":"Courtney Napoles, Keisuke Sakaguchi, Matt Post, and Joel Tetreault. 2015. Ground truth for grammatical error correction metrics. In Annual Meeting of the Association for Computational Linguistics and International Joint Conference on Natural Language Processing. 588\u2013593."},{"key":"e_1_3_2_1_38_1","volume-title":"Workshop on Asian Translation. 35\u201341","author":"Neubig Graham","year":"2015","unstructured":"Graham Neubig, Makoto Morishita, and Satoshi Nakamura. 2015. Neural Reranking Improves Subjective Quality of Machine Translation: NAIST at WAT2015. In Workshop on Asian Translation. 35\u201341."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510096"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i15.17606"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i1.16119"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.168"},{"key":"e_1_3_2_1_43_1","volume-title":"Annual Meeting of the Association for Computational Linguistics. 311\u2013318","author":"Papineni Kishore","year":"2002","unstructured":"Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. BLEU: a method for automatic evaluation of machine translation. In Annual Meeting of the Association for Computational Linguistics. 311\u2013318."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10347"},{"key":"e_1_3_2_1_45_1","unstructured":"Alec Radford Jeffrey Wu Rewon Child David Luan Dario Amodei Ilya Sutskever 2019. Language models are unsupervised multitask learners. OpenAI blog 1(2019) 9."},{"key":"e_1_3_2_1_46_1","first-page":"1","article-title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","volume":"21","author":"Raffel Colin","year":"2020","unstructured":"Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter\u00a0J Liu. 2020. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. Journal of Machine Learning Research 21 (2020), 1\u201367.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1017\/S1351324997001502"},{"key":"e_1_3_2_1_48_1","unstructured":"Alexander\u00a0M Rush Sumit Chopra and Jason Weston. 2015. A Neural Attention Model for Abstractive Sentence Summarization. In Empirical Methods in Natural Language Processing. 379\u2013389."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3387940.3392181"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICSE.2019.00021"},{"key":"e_1_3_2_1_51_1","first-page":"1","article-title":"An empirical study on learning bug-fixing patches in the wild via neural machine translation","volume":"28","author":"Tufano Michele","year":"2019","unstructured":"Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano\u00a0Di Penta, Martin White, and Denys Poshyvanyk. 2019. An empirical study on learning bug-fixing patches in the wild via neural machine translation. Transactions on Software Engineering 28 (2019), 1\u201329.","journal-title":"Transactions on Software Engineering"},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3510003.3510621"},{"key":"e_1_3_2_1_53_1","volume-title":"Towards Automating Code Review Activities. In International Conference on Software Engineering. 163\u2013174","author":"Tufano Rosalia","year":"2021","unstructured":"Rosalia Tufano, Luca Pascarella, Michele Tufanoy, Denys Poshyvanykz, and Gabriele Bavota. 2021. Towards Automating Code Review Activities. In International Conference on Software Engineering. 163\u2013174."},{"key":"e_1_3_2_1_54_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan\u00a0N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems. 5998\u20136008."},{"key":"e_1_3_2_1_55_1","unstructured":"Oriol Vinyals Meire Fortunato and Navdeep Jaitly. 2015. Pointer networks. Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_56_1","unstructured":"Xin Wang Yasheng Wang Fei Mi Pingyi Zhou Yao Wan Xiao Liu Li Li Hao Wu Jin Liu and Xin Jiang. 2021. SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation. arXiv preprint arXiv:2108.04556(2021)."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"crossref","unstructured":"Yue Wang Weishi Wang Shafiq Joty and Steven\u00a0C.H. Hoi. 2021. CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation. In Empirical Methods in Natural Language Processing. 8696\u20138708.","DOI":"10.18653\/v1\/2021.emnlp-main.685"},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00107"},{"key":"e_1_3_2_1_59_1","volume-title":"International Conference on Learning Representations.","author":"Yao Ziyu","year":"2021","unstructured":"Ziyu Yao, Frank\u00a0F. Xu, Pengcheng Yin, Huan Sun, and Graham Neubig. 2021. Learning Structural Edits via Incremental Tree Transformations. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1041"},{"key":"e_1_3_2_1_61_1","volume-title":"International Conference on Learning Representations.","author":"Yin Pengcheng","year":"2018","unstructured":"Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, and Alexander\u00a0L Gaunt. 2018. Learning to Represent Edits. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11915"},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.186"}],"event":{"name":"ASE '22: 37th IEEE\/ACM International Conference on Automated Software Engineering","location":"Rochester MI USA","acronym":"ASE '22"},"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\/3551349.3556955","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3551349.3556955","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3551349.3556955","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T07:56:11Z","timestamp":1755849371000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3551349.3556955"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":63,"alternative-id":["10.1145\/3551349.3556955","10.1145\/3551349"],"URL":"https:\/\/doi.org\/10.1145\/3551349.3556955","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"}}]}}