{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T16:57:12Z","timestamp":1783789032863,"version":"3.55.0"},"reference-count":309,"publisher":"Association for Computing Machinery (ACM)","issue":"12","license":[{"start":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T00:00:00Z","timestamp":1727913600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62102157"],"award-info":[{"award-number":["62102157"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"NSF","award":["2106758 and 2346158"],"award-info":[{"award-number":["2106758 and 2346158"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2024,12,31]]},"abstract":"<jats:p>Code intelligence leverages machine learning techniques to extract knowledge from extensive code corpora, with the aim of developing intelligent tools to improve the quality and productivity of computer programming. Currently, there is already a thriving research community focusing on code intelligence, with efforts ranging from software engineering, machine learning, data mining, natural language processing, and programming languages. In this paper, we conduct a comprehensive literature review on deep learning for code intelligence, from the aspects of code representation learning, deep learning techniques, and application tasks. We also benchmark several state-of-the-art neural models for code intelligence, and provide an open-source toolkit tailored for the rapid prototyping of deep-learning-based code intelligence models. In particular, we inspect the existing code intelligence models under the basis of code representation learning, and provide a comprehensive overview to enhance comprehension of the present state of code intelligence. Furthermore, we publicly release the source code and data resources to provide the community with a ready-to-use benchmark, which can facilitate the evaluation and comparison of existing and future code intelligence models (https:\/\/xcodemind.github.io). At last, we also point out several challenging and promising directions for future research.<\/jats:p>","DOI":"10.1145\/3664597","type":"journal-article","created":{"date-parts":[[2024,5,18]],"date-time":"2024-05-18T12:04:41Z","timestamp":1716033881000},"page":"1-41","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":31,"title":["Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit"],"prefix":"10.1145","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6937-4180","authenticated-orcid":false,"given":"Yao","family":"Wan","sequence":"first","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2257-9052","authenticated-orcid":false,"given":"Zhangqian","family":"Bi","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-7755-3112","authenticated-orcid":false,"given":"Yang","family":"He","sequence":"additional","affiliation":[{"name":"Simon Fraser University, Burnaby, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6972-4020","authenticated-orcid":false,"given":"Jianguo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Salesforce Inc, San Francisco, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3063-9425","authenticated-orcid":false,"given":"Hongyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Chongqing University, Chongqing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9510-6574","authenticated-orcid":false,"given":"Yulei","family":"Sui","sequence":"additional","affiliation":[{"name":"University of New South Wales, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4493-6663","authenticated-orcid":false,"given":"Guandong","family":"Xu","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3934-7605","authenticated-orcid":false,"given":"Hai","family":"Jin","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3491-5968","authenticated-orcid":false,"given":"Philip","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago, Chicago, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"e_1_3_3_2_2","unstructured":"2023. GitHub. Retrieved from https:\/\/www.github.com"},{"key":"e_1_3_3_3_2","unstructured":"2023. StackOverflow. Retrieved from https:\/\/www.stackoverflow.com"},{"key":"e_1_3_3_4_2","first-page":"2655","volume-title":"NAACL","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 NAACL. 2655\u20132668."},{"key":"e_1_3_3_5_2","first-page":"4998","volume-title":"ACL","author":"Ahmad Wasi Uddin","year":"2020","unstructured":"Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2020. A transformer-based approach for source code summarization. In ACL. 4998\u20135007."},{"key":"e_1_3_3_6_2","volume-title":"ICSE","author":"Ahmed Toufique","year":"2022","unstructured":"Toufique Ahmed and Premkumar Devanbu. 2022. Multilingual training for software engineering. In ICSE."},{"key":"e_1_3_3_7_2","first-page":"1004","volume-title":"ICSE","author":"Ahmed Toufique","year":"2024","unstructured":"Toufique Ahmed, Kunal Suresh Pai, Premkumar Devanbu, and Earl T. Barr. 2024. Automatic semantic augmentation of language model prompts (for code summarization). In ICSE. 1004\u20131004."},{"issue":"4","key":"e_1_3_3_8_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3212695","article-title":"A survey of machine learning for big code and naturalness","volume":"51","author":"Allamanis Miltiadis","year":"2018","unstructured":"Miltiadis Allamanis, Earl T. Barr, Premkumar Devanbu, and Charles Sutton. 2018. A survey of machine learning for big code and naturalness. ACM Computing Surveys (CSUR) 51, 4 (2018), 1\u201337.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"e_1_3_3_9_2","first-page":"91","volume-title":"PLDI","author":"Allamanis Miltiadis","year":"2020","unstructured":"Miltiadis Allamanis, Earl T. Barr, Soline Ducousso, and Zheng Gao. 2020. Typilus: Neural type hints. In PLDI. 91\u2013105."},{"key":"e_1_3_3_10_2","article-title":"Smartpaste: Learning to adapt source code","author":"Allamanis Miltiadis","year":"2017","unstructured":"Miltiadis Allamanis and Marc Brockschmidt. 2017. Smartpaste: Learning to adapt source code. arXiv:1705.07867 (2017).","journal-title":"arXiv:1705.07867"},{"key":"e_1_3_3_11_2","volume-title":"ICLR","author":"Allamanis Miltiadis","year":"2018","unstructured":"Miltiadis Allamanis, Marc Brockschmidt, and Mahmoud Khademi. 2018. Learning to represent programs with graphs. In ICLR."},{"key":"e_1_3_3_12_2","first-page":"2091","volume-title":"ICML","author":"Allamanis Miltiadis","year":"2016","unstructured":"Miltiadis Allamanis, Hao Peng, and Charles Sutton. 2016. A convolutional attention network for extreme summarization of source code. In ICML. 2091\u20132100."},{"key":"e_1_3_3_13_2","volume-title":"ICLR","author":"Alon Uri","year":"2018","unstructured":"Uri Alon, Shaked Brody, Omer Levy, and Eran Yahav. 2018. code2seq: Generating sequences from structured representations of code. In ICLR."},{"key":"e_1_3_3_14_2","first-page":"245","volume-title":"ICML","author":"Alon Uri","year":"2020","unstructured":"Uri Alon, Roy Sadaka, Omer Levy, and Eran Yahav. 2020. Structural language models of code. In ICML. 245\u2013256."},{"key":"e_1_3_3_15_2","first-page":"1","article-title":"x\u2018","volume":"3","author":"Alon Uri","year":"2019","unstructured":"Uri Alon, Meital Zilberstein, Omer Levy, and Eran Yahav. 2019. x\u2018. POPL 3 (2019), 1\u201329.","journal-title":"POPL"},{"issue":"2011","key":"e_1_3_3_16_2","first-page":"C2","article-title":"Why software is eating the world","volume":"20","author":"Andreessen Marc","year":"2011","unstructured":"Marc Andreessen. 2011. Why software is eating the world. Wall Street Journal 20, 2011 (2011), C2.","journal-title":"Wall Street Journal"},{"key":"e_1_3_3_17_2","unstructured":"Jacob Austin Augustus Odena Maxwell I. Nye Maarten Bosma Henryk Michalewski David Dohan Ellen Jiang Carrie J. Cai Michael Terry Quoc V. Le and Charles Sutton. 2021. Program synthesis with large language models. CoRR abs\/2108.07732 (2021)."},{"key":"e_1_3_3_18_2","volume-title":"ICLR","author":"Balog Matej","year":"2017","unstructured":"Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, and Daniel Tarlow. 2017. DeepCoder: Learning to write programs. In ICLR."},{"key":"e_1_3_3_19_2","first-page":"253","volume-title":"ICPC","author":"Bansal Aakash","year":"2021","unstructured":"Aakash Bansal, Sakib Haque, and Collin McMillan. 2021. Project-level encoding for neural source code summarization of subroutines. In ICPC. IEEE, 253\u2013264."},{"key":"e_1_3_3_20_2","first-page":"314","volume-title":"IJCNLP","author":"Barone Antonio Valerio Miceli","year":"2017","unstructured":"Antonio Valerio Miceli Barone and Rico Sennrich. 2017. A parallel corpus of Python functions and documentation strings for automated code documentation and code generation. In IJCNLP. 314\u2013319."},{"key":"e_1_3_3_21_2","first-page":"1","article-title":"AutoPandas: Neural-backed generators for program synthesis","volume":"3","author":"Bavishi Rohan","year":"2019","unstructured":"Rohan Bavishi, Caroline Lemieux, Roy Fox, Koushik Sen, and Ion Stoica. 2019. AutoPandas: Neural-backed generators for program synthesis. OOPSLA 3 (2019), 1\u201327.","journal-title":"OOPSLA"},{"key":"e_1_3_3_22_2","first-page":"726","volume-title":"ACL","author":"Beltagy Islam","year":"2016","unstructured":"Islam Beltagy and Chris Quirk. 2016. Improved semantic parsers for if-then statements. In ACL. 726\u2013736."},{"key":"e_1_3_3_23_2","first-page":"3589","volume-title":"NeurIPS","author":"Ben-Nun Tal","year":"2018","unstructured":"Tal Ben-Nun, Alice Shoshana Jakobovits, and Torsten Hoefler. 2018. Neural code comprehension: A learnable representation of code semantics. In NeurIPS. 3589\u20133601."},{"key":"e_1_3_3_24_2","first-page":"780","volume-title":"ICML","author":"Berabi Berkay","year":"2021","unstructured":"Berkay Berabi, Jingxuan He, Veselin Raychev, and Martin Vechev. 2021. TFix: Learning to fix coding errors with a text-to-text transformer. In ICML. 780\u2013791."},{"key":"e_1_3_3_25_2","article-title":"Automated correction for syntax errors in programming assignments using recurrent neural networks","author":"Bhatia Sahil","year":"2016","unstructured":"Sahil Bhatia and Rishabh Singh. 2016. Automated correction for syntax errors in programming assignments using recurrent neural networks. arXiv:1603.06129 (2016).","journal-title":"arXiv:1603.06129"},{"key":"e_1_3_3_26_2","first-page":"896","volume-title":"ICML","author":"Bielik Pavol","year":"2020","unstructured":"Pavol Bielik and Martin Vechev. 2020. Adversarial robustness for code. In ICML. 896\u2013907."},{"key":"e_1_3_3_27_2","volume-title":"ICLR","author":"Brockschmidt Marc","year":"2019","unstructured":"Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, and Oleksandr Polozov. 2019. Generative code modeling with graphs. In ICLR."},{"key":"e_1_3_3_28_2","first-page":"1","article-title":"A structural model for contextual code changes","volume":"4","author":"Brody Shaked","year":"2020","unstructured":"Shaked Brody, Uri Alon, and Eran Yahav. 2020. A structural model for contextual code changes. OOPSLA 4 (2020), 1\u201328.","journal-title":"OOPSLA"},{"key":"e_1_3_3_29_2","unstructured":"Tom B. Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell Sandhini Agarwal Ariel Herbert-Voss Gretchen Krueger Tom Henighan Rewon Child Aditya Ramesh Daniel M. Ziegler Jeffrey Wu Clemens Winter Christopher Hesse Mark Chen Eric Sigler Mateusz Litwin Scott Gray Benjamin Chess Jack Clark Christopher Berner Sam McCandlish Alec Radford Ilya Sutskever and Dario Amodei. 2020. Language models are few-shot learners. CoRR abs\/2005.14165 (2020)."},{"key":"e_1_3_3_30_2","first-page":"95","volume-title":"SANER","author":"B\u00fcch Lutz","year":"2019","unstructured":"Lutz B\u00fcch and Artur Andrzejak. 2019. Learning-based recursive aggregation of abstract syntax trees for code clone detection. In SANER. 95\u2013104."},{"key":"e_1_3_3_31_2","first-page":"422","volume-title":"SANER","author":"Bui Nghi D. Q.","year":"2019","unstructured":"Nghi D. Q. Bui, Yijun Yu, and Lingxiao Jiang. 2019. Bilateral dependency neural networks for cross-language algorithm classification. In SANER. 422\u2013433."},{"key":"e_1_3_3_32_2","first-page":"796","volume-title":"ESEC\/FSE","author":"Bui Nghi D. Q.","year":"2019","unstructured":"Nghi D. Q. Bui, Yijun Yu, and Lingxiao Jiang. 2019. SAR: Learning cross-language API mappings with little knowledge. In ESEC\/FSE. 796\u2013806."},{"key":"e_1_3_3_33_2","first-page":"1186","volume-title":"ICSE","author":"Bui Nghi D. Q.","year":"2021","unstructured":"Nghi D. Q. Bui, Yijun Yu, and Lingxiao Jiang. 2021. InferCode: Self-supervised learning of code representations by predicting subtrees. In ICSE. 1186\u20131197."},{"key":"e_1_3_3_34_2","first-page":"511","volume-title":"SIGIR","author":"Bui Nghi D. Q.","year":"2021","unstructured":"Nghi D. Q. Bui, Yijun Yu, and Lingxiao Jiang. 2021. Self-supervised contrastive learning for code retrieval and summarization via semantic-preserving transformations. In SIGIR. ACM, 511\u2013521."},{"key":"e_1_3_3_35_2","first-page":"3977","volume-title":"IJCAI","author":"Cai Ruichu","year":"2018","unstructured":"Ruichu Cai, Boyan Xu, Zhenjie Zhang, Xiaoyan Yang, Zijian Li, and Zhihao Liang. 2018. An encoder-decoder framework translating natural language to database queries. In IJCAI. 3977\u20133983."},{"key":"e_1_3_3_36_2","first-page":"964","volume-title":"ESEC\/FSE","author":"Cambronero Jose","year":"2019","unstructured":"Jose Cambronero, Hongyu Li, Seohyun Kim, Koushik Sen, and Satish Chandra. 2019. When deep learning met code search. In ESEC\/FSE. 964\u2013974."},{"key":"e_1_3_3_37_2","first-page":"1456","volume-title":"ICSE","author":"Cao Sicong","year":"2022","unstructured":"Sicong Cao, Xiaobing Sun, Lili Bo, Rongxin Wu, Bin Li, and Chuanqi Tao. 2022. MVD: Memory-related vulnerability detection based on flow-sensitive graph neural networks. In ICSE. 1456\u20131468."},{"key":"e_1_3_3_38_2","article-title":"On evaluating adversarial robustness","author":"Carlini Nicholas","year":"2019","unstructured":"Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian Goodfellow, Aleksander Madry, and Alexey Kurakin. 2019. On evaluating adversarial robustness. arXiv:1902.06705 (2019).","journal-title":"arXiv:1902.06705"},{"key":"e_1_3_3_39_2","article-title":"Quantifying memorization across neural language models","author":"Carlini Nicholas","year":"2022","unstructured":"Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, and Chiyuan Zhang. 2022. Quantifying memorization across neural language models. arXiv preprint arXiv:2202.07646 (2022).","journal-title":"arXiv preprint arXiv:2202.07646"},{"key":"e_1_3_3_40_2","unstructured":"Nicholas Carlini Florian Tram\u00e8r Eric Wallace Matthew Jagielski Ariel Herbert-Voss Katherine Lee Adam Roberts Tom B. Brown Dawn Song \u00dalfar Erlingsson Alina Oprea and Colin Raffel. 2021. Extracting training data from large language models. In USENIX Security Symposium. 2633\u20132650."},{"key":"e_1_3_3_41_2","first-page":"39","volume-title":"S&P","author":"Carlini Nicholas","year":"2017","unstructured":"Nicholas Carlini and David Wagner. 2017. Towards evaluating the robustness of neural networks. In S&P. 39\u201357."},{"key":"e_1_3_3_42_2","article-title":"ERNIE-code: Beyond English-centric cross-lingual pretraining for programming languages","author":"Chai Yekun","year":"2022","unstructured":"Yekun Chai, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, and Hua Wu. 2022. ERNIE-code: Beyond English-centric cross-lingual pretraining for programming languages. arXiv preprint arXiv:2212.06742 (2022).","journal-title":"arXiv preprint arXiv:2212.06742"},{"key":"e_1_3_3_43_2","first-page":"487","volume-title":"ICSE","author":"Chai Yitian","year":"2022","unstructured":"Yitian Chai, Hongyu Zhang, Beijun Shen, and Xiaodong Gu. 2022. Cross-domain deep code search with meta learning. In ICSE. 487\u2013498."},{"key":"e_1_3_3_44_2","article-title":"Codit: Code editing with tree-based neural models","author":"Chakraborty Saikat","year":"2020","unstructured":"Saikat Chakraborty, Yangruibo Ding, Miltiadis Allamanis, and Baishakhi Ray. 2020. Codit: Code editing with tree-based neural models. TSE (2020).","journal-title":"TSE"},{"key":"e_1_3_3_45_2","first-page":"443","volume-title":"ASE","author":"Chakraborty Saikat","year":"2021","unstructured":"Saikat Chakraborty and Baishakhi Ray. 2021. On multi-modal learning of editing source code. In ASE. IEEE, 443\u2013455."},{"key":"e_1_3_3_46_2","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1145\/3524610.3527917","volume-title":"ICPC","author":"Chen Fuxiang","year":"2022","unstructured":"Fuxiang Chen, Fatemeh H. Fard, David Lo, and Timofey Bryksin. 2022. On the transferability of pre-trained language models for low-resource programming languages. In ICPC. ACM, 401\u2013412."},{"key":"e_1_3_3_47_2","unstructured":"Mark Chen Jerry Tworek Heewoo Jun Qiming Yuan Henrique Pond\u00e9 de Oliveira Pinto Jared Kaplan Harrison Edwards Yuri Burda Nicholas Joseph Greg Brockman Alex Ray Raul Puri Gretchen Krueger Michael Petrov Heidy Khlaaf Girish Sastry Pamela Mishkin Brooke Chan Scott Gray Nick Ryder Mikhail Pavlov Alethea Power Lukasz Kaiser Mohammad Bavarian Clemens Winter Philippe Tillet Felipe Petroski Such Dave Cummings Matthias Plappert Fotios Chantzis Elizabeth Barnes Ariel Herbert-Voss William Hebgen Guss Alex Nichol Alex Paino Nikolas Tezak Jie Tang Igor Babuschkin Suchir Balaji Shantanu Jain William Saunders Christopher Hesse Andrew N. Carr Jan Leike Joshua Achiam Vedant Misra Evan Morikawa Alec Radford Matthew Knight Miles Brundage Mira Murati Katie Mayer Peter Welinder Bob McGrew Dario Amodei Sam McCandlish Ilya Sutskever and Wojciech Zaremba. 2021. Evaluating large language models trained on code. CoRR abs\/2107.03374 (2021)."},{"key":"e_1_3_3_48_2","first-page":"2552","volume-title":"NeurIPS","author":"Chen Xinyun","year":"2018","unstructured":"Xinyun Chen, Chang Liu, and Dawn Song. 2018. Tree-to-tree neural networks for program translation. In NeurIPS. 2552\u20132562."},{"key":"e_1_3_3_49_2","unstructured":"Zimin Chen Vincent J. 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 NeurIPS. 23089\u201323101."},{"key":"e_1_3_3_50_2","article-title":"Sequencer: Sequence-to-sequence learning for end-to-end program repair","author":"Chen Zimin","year":"2019","unstructured":"Zimin Chen, Steve James Kommrusch, Michele Tufano, Louis-No\u00ebl Pouchet, Denys Poshyvanyk, and Martin Monperrus. 2019. Sequencer: Sequence-to-sequence learning for end-to-end program repair. TSE (2019).","journal-title":"TSE"},{"issue":"3","key":"e_1_3_3_51_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3436877","article-title":"DeepWukong: Statically detecting software vulnerabilities using deep graph neural network","volume":"30","author":"Cheng Xiao","year":"2021","unstructured":"Xiao Cheng, Haoyu Wang, Jiayi Hua, Guoai Xu, and Yulei Sui. 2021. DeepWukong: Statically detecting software vulnerabilities using deep graph neural network. TOSEM 30, 3 (2021), 1\u201333.","journal-title":"TOSEM"},{"key":"e_1_3_3_52_2","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/978-1-0716-0826-5_3","article-title":"Siamese neural networks: An overview","author":"Chicco Davide","year":"2021","unstructured":"Davide Chicco. 2021. Siamese neural networks: An overview. Artificial Neural Networks (2021), 73\u201394.","journal-title":"Artificial Neural Networks"},{"key":"e_1_3_3_53_2","first-page":"703","volume-title":"ESEC\/FSE","author":"Chirkova Nadezhda","year":"2021","unstructured":"Nadezhda Chirkova and Sergey Troshin. 2021. Empirical study of transformers for source code. In ESEC\/FSE. 703\u2013715."},{"key":"e_1_3_3_54_2","first-page":"278","volume-title":"NAACL","author":"Chirkova Nadezhda","year":"2021","unstructured":"Nadezhda Chirkova and Sergey Troshin. 2021. A simple approach for handling out-of-vocabulary identifiers in deep learning for source code. In NAACL. 278\u2013288."},{"key":"e_1_3_3_55_2","unstructured":"Aakanksha Chowdhery Sharan Narang Jacob Devlin Maarten Bosma Gaurav Mishra Adam Roberts Paul Barham Hyung Won Chung Charles Sutton Sebastian Gehrmann Parker Schuh Kensen Shi Sasha Tsvyashchenko Joshua Maynez Abhishek Rao Parker Barnes Yi Tay Noam Shazeer Vinodkumar Prabhakaran Emily Reif Nan Du Ben Hutchinson Reiner Pope James Bradbury Jacob Austin Michael Isard Guy Gur-Ari Pengcheng Yin Toju Duke Anselm Levskaya Sanjay Ghemawat Sunipa Dev Henryk Michalewski Xavier Garcia Vedant Misra Kevin Robinson Liam Fedus Denny Zhou Daphne Ippolito David Luan Hyeontaek Lim Barret Zoph Alexander Spiridonov Ryan Sepassi David Dohan Shivani Agrawal Mark Omernick Andrew M. Dai Thanumalayan Sankaranarayana Pillai Marie Pellat Aitor Lewkowycz Erica Moreira Rewon Child Oleksandr Polozov Katherine Lee Zongwei Zhou Xuezhi Wang Brennan Saeta Mark Diaz Orhan Firat Michele Catasta Jason Wei Kathy Meier-Hellstern Douglas Eck Jeff Dean Slav Petrov and Noah Fiedel. 2022. PaLM: Scaling language modeling with pathways. CoRR abs\/2204.02311 (2022)."},{"key":"e_1_3_3_56_2","unstructured":"Fenia Christopoulou Gerasimos Lampouras Milan Gritta Guchun Zhang Yinpeng Guo Zhongqi Li Qi Zhang Meng Xiao Bo Shen Lin Li Hao Yu Li Yan Pingyi Zhou Xin Wang Yuchi Ma Ignacio Iacobacci Yasheng Wang Guangtai Liang Jiansheng Wei Xin Jiang Qianxiang Wang and Qun Liu. 2022. PanGu-Coder: Program synthesis with function-level language modeling. CoRR abs\/2207.11280 (2022)."},{"key":"e_1_3_3_57_2","first-page":"456","volume-title":"SANER","author":"Ciurumelea Adelina","year":"2020","unstructured":"Adelina Ciurumelea, Sebastian Proksch, and Harald C. Gall. 2020. Suggesting comment completions for Python using neural language models. In SANER. 456\u2013467."},{"key":"e_1_3_3_58_2","first-page":"60","volume-title":"ICPC","author":"Cui Nan","year":"2022","unstructured":"Nan Cui, Yuze Jiang, Xiaodong Gu, and Beijun Shen. 2022. Zero-shot program representation learning. In ICPC. ACM, 60\u201370."},{"key":"e_1_3_3_59_2","volume-title":"ICML","author":"Cummins Chris","year":"2021","unstructured":"Chris Cummins, Zacharias Fisches, Tal Ben-Nun, Torsten Hoefler, Michael O\u2019Boyle, and Hugh Leather. 2021. ProGraML: A graph-based program representation for data flow analysis and compiler optimizations. In ICML."},{"key":"e_1_3_3_60_2","first-page":"86","volume-title":"CGO","author":"Cummins Chris","year":"2017","unstructured":"Chris Cummins, Pavlos Petoumenos, Zheng Wang, and Hugh Leather. 2017. Synthesizing benchmarks for predictive modeling. In CGO. 86\u201399."},{"key":"e_1_3_3_61_2","first-page":"1475","volume-title":"ICML","author":"Cvitkovic Milan","year":"2019","unstructured":"Milan Cvitkovic, Badal Singh, and Animashree Anandkumar. 2019. Open vocabulary learning on source code with a graph-structured cache. In ICML. 1475\u20131485."},{"key":"e_1_3_3_62_2","article-title":"Automatic feature learning for predicting vulnerable software components","author":"Dam Hoa Khanh","year":"2018","unstructured":"Hoa Khanh Dam, Truyen Tran, Trang Thi Minh Pham, Shien Wee Ng, John Grundy, and Aditya Ghose. 2018. Automatic feature learning for predicting vulnerable software components. TSE (2018).","journal-title":"TSE"},{"key":"e_1_3_3_63_2","first-page":"396","volume-title":"SANER","author":"Deng Zhongyang","year":"2022","unstructured":"Zhongyang Deng, Ling Xu, Chao Liu, Meng Yan, Zhou Xu, and Yan Lei. 2022. Fine-grained co-attentive representation learning for semantic code search. In SANER. 396\u2013407."},{"key":"e_1_3_3_64_2","article-title":"Deep learning & software engineering: State of research and future directions","volume":"2009","author":"Devanbu Prem","year":"2020","unstructured":"Prem Devanbu, Matthew B. Dwyer, Sebastian G. Elbaum, Michael Lowry, Kevin Moran, et\u00a0al. 2020. Deep learning & software engineering: State of research and future directions. CoRR abs\/2009.08525 (2020).","journal-title":"CoRR"},{"key":"e_1_3_3_65_2","first-page":"4171","volume-title":"NAACL","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In NAACL. 4171\u20134186."},{"key":"e_1_3_3_66_2","first-page":"990","volume-title":"ICML","author":"Devlin Jacob","year":"2017","unstructured":"Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, and Pushmeet Kohli. 2017. Robustfill: Neural program learning under noisy i\/o. In ICML. 990\u2013998."},{"key":"e_1_3_3_67_2","volume-title":"ICLR","author":"Dinella Elizabeth","year":"2020","unstructured":"Elizabeth Dinella, Hanjun Dai, Ziyang Li, Mayur Naik, Le Song, and Ke Wang. 2020. Hoppity: Learning graph transformations to detect and fix bugs in programs. In ICLR."},{"key":"e_1_3_3_68_2","first-page":"6300","volume-title":"ACL","author":"Ding Yangruibo","year":"2022","unstructured":"Yangruibo Ding, Luca Buratti, Saurabh Pujar, Alessandro Morari, Baishakhi Ray, and Saikat Chakraborty. 2022. Towards learning (Dis)-similarity of source code from program contrasts. In ACL. 6300\u20136312."},{"key":"e_1_3_3_69_2","article-title":"Vulnerability detection with code language models: How far are we?","author":"Ding Yangruibo","year":"2024","unstructured":"Yangruibo Ding, Yanjun Fu, Omniyyah Ibrahim, Chawin Sitawarin, Xinyun Chen, Basel Alomair, David Wagner, Baishakhi Ray, and Yizheng Chen. 2024. Vulnerability detection with code language models: How far are we? arXiv preprint arXiv:2403.18624 (2024).","journal-title":"arXiv preprint arXiv:2403.18624"},{"key":"e_1_3_3_70_2","volume-title":"ACL","author":"Dong Li","year":"2016","unstructured":"Li Dong and Mirella Lapata. 2016. Language to logical form with neural attention. In ACL."},{"key":"e_1_3_3_71_2","article-title":"ClassEval: A manually-crafted benchmark for evaluating LLMs on class-level code generation","author":"Du Xueying","year":"2023","unstructured":"Xueying Du, Mingwei Liu, Kaixin Wang, Hanlin Wang, Junwei Liu, Yixuan Chen, Jiayi Feng, Chaofeng Sha, Xin Peng, and Yiling Lou. 2023. ClassEval: A manually-crafted benchmark for evaluating LLMs on class-level code generation. arXiv preprint arXiv:2308.01861 (2023).","journal-title":"arXiv preprint arXiv:2308.01861"},{"key":"e_1_3_3_72_2","first-page":"1625","volume-title":"CVPR","author":"Eykholt Kevin","year":"2018","unstructured":"Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Amir Rahmati, Chaowei Xiao, Atul Prakash, Tadayoshi Kohno, and Dawn Song. 2018. Robust physical-world attacks on deep learning visual classification. In CVPR. 1625\u20131634."},{"key":"e_1_3_3_73_2","first-page":"1536","volume-title":"Findings of EMNLP","author":"Feng Zhangyin","year":"2020","unstructured":"Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, et\u00a0al. 2020. CodeBERT: A pre-trained model for programming and natural languages. In Findings of EMNLP. 1536\u20131547."},{"key":"e_1_3_3_74_2","volume-title":"ICLR","author":"Fernandes Patrick","year":"2019","unstructured":"Patrick Fernandes, Miltiadis Allamanis, and Marc Brockschmidt. 2019. Structured neural summarization. In ICLR."},{"key":"e_1_3_3_75_2","volume-title":"ICLR","author":"Fried Daniel","year":"2023","unstructured":"Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Scott Yih, Luke Zettlemoyer, and Mike Lewis. 2023. InCoder: A generative model for code infilling and synthesis. In ICLR."},{"key":"e_1_3_3_76_2","first-page":"935","volume-title":"ESEC\/FSE","author":"Fu Michael","year":"2022","unstructured":"Michael Fu, Chakkrit Tantithamthavorn, Trung Le, Van Nguyen, and Dinh Q. Phung. 2022. VulRepair: A T5-based automated software vulnerability repair. In ESEC\/FSE. 935\u2013947."},{"key":"e_1_3_3_77_2","first-page":"24","volume-title":"ICPC","author":"Gao Yuexiu","year":"2022","unstructured":"Yuexiu Gao and Chen Lyu. 2022. M2TS: Multi-scale multi-modal approach based on transformer for source code summarization. In ICPC. ACM, 24\u201335."},{"key":"e_1_3_3_78_2","first-page":"218","volume-title":"ESEC\/FSE","author":"Gao Zhipeng","year":"2021","unstructured":"Zhipeng Gao, Xin Xia, David Lo, John Grundy, and Thomas Zimmermann. 2021. Automating the removal of obsolete TODO comments. In ESEC\/FSE. 218\u2013229."},{"key":"e_1_3_3_79_2","first-page":"1","volume-title":"Proceedings of Workshop for NLP Open Source Software (NLP-OSS)","author":"Gardner Matt","year":"2018","unstructured":"Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, et\u00a0al. 2018. AllenNLP: A deep semantic natural language processing platform. In Proceedings of Workshop for NLP Open Source Software (NLP-OSS). 1\u20136."},{"key":"e_1_3_3_80_2","first-page":"39:1\u201339:13","volume-title":"ICSE","author":"Geng Mingyang","year":"2024","unstructured":"Mingyang Geng, Shangwen Wang, Dezun Dong, Haotian Wang, Ge Li, Zhi Jin, Xiaoguang Mao, and Xiangke Liao. 2024. Large language models are few-shot summarizers: Multi-intent comment generation via in-context learning. In ICSE. 39:1\u201339:13."},{"key":"e_1_3_3_81_2","first-page":"13","volume-title":"SANER","author":"Gong Zi","year":"2022","unstructured":"Zi Gong, Cuiyun Gao, Yasheng Wang, Wenchao Gu, Yun Peng, and Zenglin Xu. 2022. Source code summarization with structural relative position guided transformer. In SANER. 13\u201324."},{"key":"e_1_3_3_82_2","article-title":"Neural Turing machines","author":"Graves Alex","year":"2014","unstructured":"Alex Graves, Greg Wayne, and Ivo Danihelka. 2014. Neural Turing machines. arXiv:1410.5401 (2014).","journal-title":"arXiv:1410.5401"},{"key":"e_1_3_3_83_2","first-page":"2534","volume-title":"ACL","author":"Gu Wenchao","year":"2022","unstructured":"Wenchao Gu, Yanlin Wang, Lun Du, Hongyu Zhang, Shi Han, Dongmei Zhang, and Michael R. Lyu. 2022. Accelerating code search with deep hashing and code classification. In ACL. 2534\u20132544."},{"key":"e_1_3_3_84_2","first-page":"933","volume-title":"ICSE","author":"Gu Xiaodong","year":"2018","unstructured":"Xiaodong Gu, Hongyu Zhang, and Sunghun Kim. 2018. Deep code search. In ICSE. 933\u2013944."},{"key":"e_1_3_3_85_2","first-page":"631","volume-title":"Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering","author":"Gu Xiaodong","year":"2016","unstructured":"Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, and Sunghun Kim. 2016. Deep API learning. In Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering. 631\u2013642."},{"key":"e_1_3_3_86_2","first-page":"3675","volume-title":"IJCAI","author":"Gu Xiaodong","year":"2017","unstructured":"Xiaodong Gu, Hongyu Zhang, Dongmei Zhang, and Sunghun Kim. 2017. DeepAM: Migrate APIs with multi-modal sequence to sequence learning. In IJCAI. 3675\u20133681."},{"key":"e_1_3_3_87_2","volume-title":"SANER","author":"Gui Yi","year":"2022","unstructured":"Yi Gui, Yao Wan, Hongyu Zhang, Huifang Huang, Yulei Sui, Guandong Xu, Zhiyuan Shao, and Hai Jin. 2022. Cross-language binary-source code matching with intermediate representations. In SANER."},{"key":"e_1_3_3_88_2","article-title":"Textbooks are all you need","author":"Gunasekar Suriya","year":"2023","unstructured":"Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio C\u00e9sar Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, et\u00a0al. 2023. Textbooks are all you need. arXiv preprint arXiv:2306.11644 (2023).","journal-title":"arXiv preprint arXiv:2306.11644"},{"key":"e_1_3_3_89_2","first-page":"7212","volume-title":"ACL","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 ACL. 7212\u20137225."},{"key":"e_1_3_3_90_2","volume-title":"ICLR","author":"Guo Daya","year":"2021","unstructured":"Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, et\u00a0al. 2021. GraphCodeBERT: Pre-training code representations with data flow. In ICLR."},{"key":"e_1_3_3_91_2","volume-title":"ICLR","author":"Guo Daya","year":"2022","unstructured":"Daya Guo, Alexey Svyatkovskiy, Jian Yin, Nan Duan, Marc Brockschmidt, and Miltiadis Allamanis. 2022. Learning to complete code with sketches. In ICLR."},{"key":"e_1_3_3_92_2","first-page":"486","volume-title":"ACL","author":"Guo Juncai","year":"2022","unstructured":"Juncai Guo, Jin Liu, Yao Wan, Li Li, and Pingyi Zhou. 2022. Modeling hierarchical syntax structure with triplet position for source code summarization. In ACL. 486\u2013500."},{"key":"e_1_3_3_93_2","volume-title":"NeurIPS","author":"Gupta Kavi","year":"2020","unstructured":"Kavi Gupta, Peter Ebert Christensen, Xinyun Chen, and Dawn Song. 2020. Synthesize, execute and debug: Learning to repair for neural program synthesis. In NeurIPS."},{"key":"e_1_3_3_94_2","article-title":"Deep reinforcement learning for programming language correction","author":"Gupta Rahul","year":"2018","unstructured":"Rahul Gupta, Aditya Kanade, and Shirish Shevade. 2018. Deep reinforcement learning for programming language correction. arXiv:1801.10467 (2018).","journal-title":"arXiv:1801.10467"},{"key":"e_1_3_3_95_2","article-title":"Neural attribution for semantic bug-localization in student programs","volume":"32","author":"Gupta R.","year":"2019","unstructured":"R. Gupta, A. Kanade, and S. Shevade. 2019. Neural attribution for semantic bug-localization in student programs. NeurIPS 32 (2019).","journal-title":"NeurIPS"},{"key":"e_1_3_3_96_2","volume-title":"AAAI","author":"Gupta Rahul","year":"2017","unstructured":"Rahul Gupta, Soham Pal, Aditya Kanade, and Shirish Shevade. 2017. DeepFix: Fixing common C language errors by deep learning. In AAAI."},{"key":"e_1_3_3_97_2","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1145\/3524610.3527886","volume-title":"ICPC","author":"Hadi Mohammad Abdul","year":"2022","unstructured":"Mohammad Abdul Hadi, Imam Nur Bani Yusuf, Ferdian Thung, Kien Gia Luong, Lingxiao Jiang, Fatemeh H. Fard, and David Lo. 2022. On the effectiveness of pretrained models for API learning. In ICPC. ACM, 309\u2013320."},{"key":"e_1_3_3_98_2","first-page":"8563","volume-title":"ACL","author":"Haldar Rajarshi","year":"2020","unstructured":"Rajarshi Haldar, Lingfei Wu, JinJun Xiong, and Julia Hockenmaier. 2020. A multi-perspective architecture for semantic code search. In ACL. 8563\u20138568."},{"key":"e_1_3_3_99_2","first-page":"330","volume-title":"SANER","author":"Haque Sakib","year":"2021","unstructured":"Sakib Haque, Aakash Bansal, Lingfei Wu, and Collin McMillan. 2021. Action word prediction for neural source code summarization. In SANER. IEEE, 330\u2013341."},{"key":"e_1_3_3_100_2","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1145\/3379597.3387449","volume-title":"MSR","author":"Haque Sakib","year":"2020","unstructured":"Sakib Haque, Alexander LeClair, Lingfei Wu, and Collin McMillan. 2020. Improved automatic summarization of subroutines via attention to file context. In MSR. 300\u2013310."},{"key":"e_1_3_3_101_2","first-page":"7944","volume-title":"NeurIPS","author":"Harer Jacob","year":"2018","unstructured":"Jacob Harer, Onur Ozdemir, Tomo Lazovich, Christopher P. Reale, Rebecca L. Russell, Louis Y. Kim, and Sang Peter Chin. 2018. Learning to repair software vulnerabilities with generative adversarial networks. In NeurIPS. 7944\u20137954."},{"key":"e_1_3_3_102_2","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1007\/978-3-319-96142-2_2","volume-title":"International Conference on Computer Aided Verification","author":"Hassan Mostafa","year":"2018","unstructured":"Mostafa Hassan, Caterina Urban, Marco Eilers, and Peter M\u00fcller. 2018. MaxSMT-based type inference for Python 3. In International Conference on Computer Aided Verification. 12\u201319."},{"key":"e_1_3_3_103_2","article-title":"Learning to generate corrective patches using neural machine translation","author":"Hata Hideaki","year":"2018","unstructured":"Hideaki Hata, Emad Shihab, and Graham Neubig. 2018. Learning to generate corrective patches using neural machine translation. arXiv:1812.07170 (2018).","journal-title":"arXiv:1812.07170"},{"key":"e_1_3_3_104_2","first-page":"925","volume-title":"EMNLP","author":"Hayati Shirley Anugrah","year":"2018","unstructured":"Shirley Anugrah Hayati, Raphael Olivier, Pravalika Avvaru, Pengcheng Yin, Anthony Tomasic, and Graham Neubig. 2018. Retrieval-based neural code generation. In EMNLP. 925\u2013930."},{"key":"e_1_3_3_105_2","first-page":"152","volume-title":"ESEC\/FSE","author":"Hellendoorn Vincent J.","year":"2018","unstructured":"Vincent J. Hellendoorn, Christian Bird, Earl T. Barr, and Miltiadis Allamanis. 2018. Deep learning type inference. In ESEC\/FSE. 152\u2013162."},{"key":"e_1_3_3_106_2","first-page":"163","volume-title":"ESEC\/FSE","author":"Henkel Jordan","year":"2018","unstructured":"Jordan Henkel, Shuvendu K. Lahiri, Ben Liblit, and Thomas Reps. 2018. Code vectors: Understanding programs through embedded abstracted symbolic traces. In ESEC\/FSE. 163\u2013174."},{"key":"e_1_3_3_107_2","first-page":"518","volume-title":"ICSE","author":"Hoang Thong","year":"2020","unstructured":"Thong Hoang, Hong Jin Kang, David Lo, and Julia Lawall. 2020. CC2Vec: Distributed representations of code changes. In ICSE. 518\u2013529."},{"key":"e_1_3_3_108_2","first-page":"200","volume-title":"ICPC","author":"Hu Xing","year":"2018","unstructured":"Xing Hu, Ge Li, Xin Xia, David Lo, and Zhi Jin. 2018. Deep code comment generation. In ICPC. 200\u201320010."},{"key":"e_1_3_3_109_2","first-page":"2269","volume-title":"IJCAI","author":"Hu Xing","year":"2018","unstructured":"Xing Hu, Ge Li, Xin Xia, David Lo, Shuai Lu, and Zhi Jin. 2018. Summarizing source code with transferred API knowledge. (2018). In IJCAI, Vol. 19. 2269\u20132275."},{"key":"e_1_3_3_110_2","first-page":"109:1\u2013109:12","volume-title":"ASE","author":"Hu Yutao","year":"2022","unstructured":"Yutao Hu, Deqing Zou, Junru Peng, Yueming Wu, Junjie Shan, and Hai Jin. 2022. TreeCen: Building tree graph for scalable semantic code clone detection. In ASE. ACM, 109:1\u2013109:12."},{"key":"e_1_3_3_111_2","first-page":"79:1\u201379:13","volume-title":"ASE","author":"Huang Qing","year":"2022","unstructured":"Qing Huang, Zhiqiang Yuan, Zhenchang Xing, Xiwei Xu, Liming Zhu, and Qinghua Lu. 2022. Prompt-tuned code language model as a neural knowledge base for type inference in statically-typed partial code. In ASE. ACM, 79:1\u201379:13."},{"key":"e_1_3_3_112_2","article-title":"Codesearchnet challenge: Evaluating the state of semantic code search","author":"Husain Hamel","year":"2019","unstructured":"Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt. 2019. Codesearchnet challenge: Evaluating the state of semantic code search. arXiv:1909.09436 (2019).","journal-title":"arXiv:1909.09436"},{"key":"e_1_3_3_113_2","first-page":"2073","volume-title":"ACL","author":"Iyer Srinivasan","year":"2016","unstructured":"Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, and Luke Zettlemoyer. 2016. Summarizing source code using a neural attention model. In ACL. 2073\u20132083."},{"key":"e_1_3_3_114_2","first-page":"1643","volume-title":"EMNLP","author":"Iyer Srinivasan","year":"2018","unstructured":"Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, and Luke Zettlemoyer. 2018. Mapping language to code in programmatic context. In EMNLP. 1643\u20131652."},{"key":"e_1_3_3_115_2","first-page":"5954","volume-title":"EMNLP","author":"Jain Paras","year":"2021","unstructured":"Paras Jain, Ajay Jain, Tianjun Zhang, Pieter Abbeel, Joseph Gonzalez, and Ion Stoica. 2021. Contrastive code representation learning. In EMNLP. 5954\u20135971."},{"key":"e_1_3_3_116_2","first-page":"38","volume-title":"ICSE","author":"Jiang He","year":"2017","unstructured":"He Jiang, Jingxuan Zhang, Zhilei Ren, and Tao Zhang. 2017. An unsupervised approach for discovering relevant tutorial fragments for APIs. In ICSE. 38\u201348."},{"key":"e_1_3_3_117_2","first-page":"1161","volume-title":"ICSE","author":"Jiang Nan","year":"2021","unstructured":"Nan Jiang, Thibaud Lutellier, and Lin Tan. 2021. CURE: Code-aware neural machine translation for automatic program repair. In ICSE. 1161\u20131173."},{"key":"e_1_3_3_118_2","first-page":"54","volume-title":"Uncertainty in Artificial Intelligence","author":"Jiang Xue","year":"2021","unstructured":"Xue Jiang, Zhuoran Zheng, Chen Lyu, Liang Li, and Lei Lyu. 2021. TreeBERT: A tree-based pre-trained model for programming language. In Uncertainty in Artificial Intelligence. 54\u201363."},{"key":"e_1_3_3_119_2","first-page":"1078","volume-title":"SANER","author":"Jin Dun","year":"2022","unstructured":"Dun Jin, Peiyu Liu, and Zhenfang Zhu. 2022. Automatically generating code comment using heterogeneous graph neural networks. In SANER. 1078\u20131088."},{"key":"e_1_3_3_120_2","first-page":"5110","volume-title":"ICML","author":"Kanade Aditya","year":"2020","unstructured":"Aditya Kanade, Petros Maniatis, Gogul Balakrishnan, and Kensen Shi. 2020. Learning and evaluating contextual embedding of source code. In ICML. 5110\u20135121."},{"key":"e_1_3_3_121_2","first-page":"1073","volume-title":"ICSE","author":"Karampatsis Rafael-Michael","year":"2020","unstructured":"Rafael-Michael Karampatsis, Hlib Babii, Romain Robbes, Charles Sutton, and Andrea Janes. 2020. Big code!= big vocabulary: Open-vocabulary models for source code. In ICSE. 1073\u20131085."},{"key":"e_1_3_3_122_2","first-page":"150","volume-title":"ICSE","author":"Kim Seohyun","year":"2021","unstructured":"Seohyun Kim, Jinman Zhao, Yuchi Tian, and Satish Chandra. 2021. Code prediction by feeding trees to transformers. In ICSE. 150\u2013162."},{"key":"e_1_3_3_123_2","first-page":"14967","volume-title":"NeurIPS","author":"Lachaux Marie-Anne","year":"2021","unstructured":"Marie-Anne Lachaux, Baptiste Rozi\u00e8re, Marc Szafraniec, and Guillaume Lample. 2021. DOBF: A deobfuscation pre-training objective for programming languages. In NeurIPS. 14967\u201314979."},{"key":"e_1_3_3_124_2","first-page":"75","volume-title":"CGO","author":"Lattner Chris","year":"2004","unstructured":"Chris Lattner and Vikram Adve. 2004. LLVM: A compilation framework for lifelong program analysis & transformation. In CGO. 75\u201386."},{"key":"e_1_3_3_125_2","volume-title":"ICLR","author":"Le Tue","year":"2018","unstructured":"Tue Le, Tuan Nguyen, Trung Le, Dinh Phung, Paul Montague, Olivier De Vel, and Lizhen Qu. 2018. Maximal divergence sequential autoencoder for binary software vulnerability detection. In ICLR."},{"key":"e_1_3_3_126_2","first-page":"461","volume-title":"ICSME","author":"LeClair Alexander","year":"2018","unstructured":"Alexander LeClair, Zachary Eberhart, and Collin McMillan. 2018. Adapting neural text classification for improved software categorization. In ICSME. 461\u2013472."},{"key":"e_1_3_3_127_2","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1145\/3387904.3389268","volume-title":"ICPC","author":"LeClair Alexander","year":"2020","unstructured":"Alexander LeClair, Sakib Haque, Lingfei Wu, and Collin McMillan. 2020. Improved code summarization via a graph neural network. In ICPC. 184\u2013195."},{"key":"e_1_3_3_128_2","first-page":"795","volume-title":"ICSE","author":"LeClair Alexander","year":"2019","unstructured":"Alexander LeClair, Siyuan Jiang, and Collin McMillan. 2019. A neural model for generating natural language summaries of program subroutines. In ICSE. 795\u2013806."},{"key":"e_1_3_3_129_2","first-page":"7871","volume-title":"ACL","author":"Lewis Mike","year":"2020","unstructured":"Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, et\u00a0al. 2020. BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In ACL. 7871\u20137880."},{"key":"e_1_3_3_130_2","article-title":"Enabling programming thinking in large language models toward code generation","author":"Li Jia","year":"2023","unstructured":"Jia Li, Ge Li, Yongmin Li, and Zhi Jin. 2023. Enabling programming thinking in large language models toward code generation. arXiv preprint arXiv:2305.06599 (2023).","journal-title":"arXiv preprint arXiv:2305.06599"},{"key":"e_1_3_3_131_2","article-title":"Large language model-aware in-context learning for code generation","author":"Li Jia","year":"2023","unstructured":"Jia Li, Ge Li, Chongyang Tao, Huangzhao Zhang, Fang Liu, and Zhi Jin. 2023. Large language model-aware in-context learning for code generation. arXiv preprint arXiv:2310.09748 (2023).","journal-title":"arXiv preprint arXiv:2310.09748"},{"key":"e_1_3_3_132_2","first-page":"155","volume-title":"ASE","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 ASE. 155\u2013166."},{"key":"e_1_3_3_133_2","first-page":"4159","volume-title":"IJCAI","author":"Li Jian","year":"2018","unstructured":"Jian Li, Yue Wang, Michael R. Lyu, and Irwin King. 2018. Code completion with neural attention and pointer networks. In IJCAI. 4159\u20134165."},{"key":"e_1_3_3_134_2","first-page":"1009","volume-title":"ESEC\/FSE","author":"Li Lingwei","year":"2022","unstructured":"Lingwei Li, Li Yang, Huaxi Jiang, Jun Yan, Tiejian Luo, Zihan Hua, Geng Liang, and Chun Zuo. 2022. AUGER: Automatically generating review comments with pre-training models. In ESEC\/FSE. 1009\u20131021."},{"key":"e_1_3_3_135_2","article-title":"StarCoder: May the source be with you!","author":"Li Raymond","year":"2023","unstructured":"Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, et\u00a0al. 2023. StarCoder: May the source be with you! arXiv preprint arXiv:2305.06161 (2023).","journal-title":"arXiv preprint arXiv:2305.06161"},{"key":"e_1_3_3_136_2","volume-title":"EMNLP","author":"Li Xiaonan","year":"2022","unstructured":"Xiaonan Li, Yeyun Gong, Yelong Shen, et\u00a0al. 2022. CodeRetriever: Unimodal and bimodal contrastive learning. In EMNLP."},{"issue":"6624","key":"e_1_3_3_137_2","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1126\/science.abq1158","article-title":"Competition-level code generation with AlphaCode","volume":"378","author":"Li Yujia","year":"2022","unstructured":"Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, R\u00e9mi Leblond, Tom Eccles, et\u00a0al. 2022. Competition-level code generation with AlphaCode. Science 378, 6624 (2022), 1092\u20131097.","journal-title":"Science"},{"key":"e_1_3_3_138_2","volume-title":"ICLR","author":"Li Yujia","year":"2016","unstructured":"Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard S. Zemel. 2016. Gated graph sequence neural networks. In ICLR."},{"key":"e_1_3_3_139_2","first-page":"602","volume-title":"ICSE","author":"Li Yi","year":"2020","unstructured":"Yi Li, Shaohua Wang, and Tien N. Nguyen. 2020. DLFix: Context-based code transformation learning for automated program repair. In ICSE. 602\u2013614."},{"key":"e_1_3_3_140_2","first-page":"661","volume-title":"ICSE","author":"Li Yi","year":"2021","unstructured":"Yi Li, Shaohua Wang, and Tien N. Nguyen. 2021. Fault localization with code coverage representation learning. In ICSE. 661\u2013673."},{"key":"e_1_3_3_141_2","first-page":"292","volume-title":"ESEC\/FSE","author":"Li Yi","year":"2021","unstructured":"Yi Li, Shaohua Wang, and Tien N. Nguyen. 2021. Vulnerability detection with fine-grained interpretations. In ESEC\/FSE. 292\u2013303."},{"key":"e_1_3_3_142_2","first-page":"511","volume-title":"ICSE","author":"Li Yi","year":"2022","unstructured":"Yi Li, Shaohua Wang, and Tien N. Nguyen. 2022. DEAR: A novel deep learning-based approach for automated program repair. In ICSE. 511\u2013523."},{"key":"e_1_3_3_143_2","first-page":"1","article-title":"Improving bug detection via context-based code representation learning and attention-based neural networks","volume":"3","author":"Li Yi","year":"2019","unstructured":"Yi Li, Shaohua Wang, Tien N. Nguyen, and Son Van Nguyen. 2019. Improving bug detection via context-based code representation learning and attention-based neural networks. OOPSLA 3 (2019), 1\u201330.","journal-title":"OOPSLA"},{"key":"e_1_3_3_144_2","first-page":"2253","volume-title":"ICSE","author":"Li Zongjie","year":"2022","unstructured":"Zongjie Li, Pingchuan Ma, Huaijin Wang, Shuai Wang, Qiyi Tang, Sen Nie, and Shi Wu. 2022. Unleashing the power of compiler intermediate representation to enhance neural program embeddings. In ICSE. 2253\u20132265."},{"key":"e_1_3_3_145_2","article-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. TDSC (2021).","journal-title":"TDSC"},{"key":"e_1_3_3_146_2","volume-title":"NDSS","author":"Li Zhen","year":"2018","unstructured":"Zhen Li, Deqing Zou, Shouhuai Xu, Xinyu Ou, Hai Jin, Sujuan Wang, Zhijun Deng, and Yuyi Zhong. 2018. VulDeePecker: A deep learning-based system for vulnerability detection. In NDSS."},{"key":"e_1_3_3_147_2","first-page":"184","volume-title":"ICPC","author":"Lin Chen","year":"2021","unstructured":"Chen Lin, Zhichao Ouyang, Junqing Zhuang, Jianqiang Chen, Hui Li, and Rongxin Wu. 2021. Improving code summarization with block-wise abstract syntax tree splitting. In ICPC. IEEE, 184\u2013195."},{"key":"e_1_3_3_148_2","first-page":"36","volume-title":"SANER","author":"Ling Chunyang","year":"2021","unstructured":"Chunyang Ling, Yanzhen Zou, and Bing Xie. 2021. Graph neural network based collaborative filtering for API usage recommendation. In SANER. IEEE, 36\u201347."},{"key":"e_1_3_3_149_2","first-page":"599","volume-title":"ACL","author":"Ling Wang","year":"2016","unstructured":"Wang Ling, Phil Blunsom, Edward Grefenstette, Karl Moritz Hermann, Tom\u00e1\u0161 Ko\u010disk\u1ef3, Fumin Wang, and Andrew Senior. 2016. Latent predictor networks for code generation. In ACL. 599\u2013609."},{"issue":"5","key":"e_1_3_3_150_2","first-page":"88:1\u201388:21","article-title":"Deep graph matching and searching for semantic code retrieval","volume":"15","author":"Ling Xiang","year":"2021","unstructured":"Xiang Ling, Lingfei Wu, Saizhuo Wang, Gaoning Pan, Tengfei Ma, Fangli Xu, Alex X. Liu, Chunming Wu, and Shouling Ji. 2021. Deep graph matching and searching for semantic code retrieval. TKDD 15, 5 (2021), 88:1\u201388:21.","journal-title":"TKDD"},{"key":"e_1_3_3_151_2","article-title":"Improving ChatGPT prompt for code generation","author":"Liu Chao","year":"2023","unstructured":"Chao Liu, Xuanlin Bao, Hongyu Zhang, Neng Zhang, Haibo Hu, Xiaohong Zhang, and Meng Yan. 2023. Improving ChatGPT prompt for code generation. arXiv preprint arXiv:2305.08360 (2023).","journal-title":"arXiv preprint arXiv:2305.08360"},{"key":"e_1_3_3_152_2","first-page":"4574","article-title":"Latent attention for if-then program synthesis","volume":"29","author":"Liu Chang","year":"2016","unstructured":"Chang Liu, Xinyun Chen, Eui Chul Shin, Mingcheng Chen, and Dawn Song. 2016. Latent attention for if-then program synthesis. NeurIPS 29 (2016), 4574\u20134582.","journal-title":"NeurIPS"},{"key":"e_1_3_3_153_2","unstructured":"Chang Liu Xin Wang Richard Shin Joseph E. Gonzalez and Dawn Song. 2016. Neural code completion. OpenReview.net (2016)."},{"key":"e_1_3_3_154_2","first-page":"37","volume-title":"ICPC","author":"Liu Fang","year":"2020","unstructured":"Fang Liu, Ge Li, Bolin Wei, Xin Xia, Zhiyi Fu, and Zhi Jin. 2020. A self-attentional neural architecture for code completion with multi-task learning. In ICPC. 37\u201347."},{"key":"e_1_3_3_155_2","first-page":"473","volume-title":"ASE","author":"Liu Fang","year":"2020","unstructured":"Fang Liu, Ge Li, Yunfei Zhao, and Zhi Jin. 2020. Multi-task learning based pre-trained language model for code completion. In ASE. 473\u2013485."},{"key":"e_1_3_3_156_2","doi-asserted-by":"crossref","first-page":"110547","DOI":"10.1016\/j.jss.2020.110547","article-title":"Modeling programs hierarchically with stack-augmented LSTM","volume":"164","author":"Liu Fang","year":"2020","unstructured":"Fang Liu, Lu Zhang, and Zhi Jin. 2020. Modeling programs hierarchically with stack-augmented LSTM. Journal of Systems and Software 164 (2020), 110547.","journal-title":"Journal of Systems and Software"},{"key":"e_1_3_3_157_2","first-page":"1","volume-title":"ICSE","author":"Liu Kui","year":"2019","unstructured":"Kui Liu, Dongsun Kim, Tegawend\u00e9 F. Bissyand\u00e9, Taeyoung Kim, Kisub Kim, Anil Koyuncu, Suntae Kim, and Yves Le Traon. 2019. Learning to spot and refactor inconsistent method names. In ICSE. 1\u201312."},{"key":"e_1_3_3_158_2","article-title":"A practical black-box attack on source code authorship identification classifiers","author":"Liu Qianjun","year":"2021","unstructured":"Qianjun Liu, Shouling Ji, Changchang Liu, and Chunming Wu. 2021. A practical black-box attack on source code authorship identification classifiers. TIFS (2021).","journal-title":"TIFS"},{"key":"e_1_3_3_159_2","volume-title":"ICLR","author":"Liu Shangqing","year":"2020","unstructured":"Shangqing Liu, Yu Chen, Xiaofei Xie, Jing Kai Siow, and Yang Liu. 2020. Retrieval-augmented generation for code summarization via hybrid GNN. In ICLR."},{"key":"e_1_3_3_160_2","article-title":"Combining graph neural networks with expert knowledge for smart contract vulnerability detection","author":"Liu Zhenguang","year":"2021","unstructured":"Zhenguang Liu, Peng Qian, Xiaoyang Wang, Yuan Zhuang, Lin Qiu, and Xun Wang. 2021. Combining graph neural networks with expert knowledge for smart contract vulnerability detection. TKDE (2021).","journal-title":"TKDE"},{"key":"e_1_3_3_161_2","first-page":"585","volume-title":"ASE","author":"Liu Zhongxin","year":"2020","unstructured":"Zhongxin Liu, Xin Xia, Meng Yan, and Shanping Li. 2020. Automating just-in-time comment updating. In ASE. 585\u2013597."},{"key":"e_1_3_3_162_2","volume-title":"ASE","author":"L\u00f3pez Jos\u00e9 Antonio Hern\u00e1ndez","year":"2022","unstructured":"Jos\u00e9 Antonio Hern\u00e1ndez L\u00f3pez, Martin Weyssow, Jes\u00fas S\u00e1nchez Cuadrado, and Houari A. Sahraoui. 2022. AST-probe: Recovering abstract syntax trees from hidden representations of pre-trained language models. In ASE."},{"key":"e_1_3_3_163_2","first-page":"6227","volume-title":"ACL","author":"Lu Shuai","year":"2022","unstructured":"Shuai Lu, Nan Duan, Hojae Han, Daya Guo, Seung-won Hwang, and Alexey Svyatkovskiy. 2022. ReACC: A retrieval-augmented code completion framework. In ACL. 6227\u20136240."},{"key":"e_1_3_3_164_2","volume-title":"NeurIPS Datasets and Benchmarks","author":"Lu Shuai","year":"2021","unstructured":"Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, et\u00a0al. 2021. CodeXGLUE: A machine learning benchmark dataset for code understanding and generation. In NeurIPS Datasets and Benchmarks."},{"key":"e_1_3_3_165_2","article-title":"WizardCoder: Empowering code large language models with evol-instruct","author":"Luo Ziyang","year":"2023","unstructured":"Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Chongyang Tao, Jing Ma, Qingwei Lin, and Daxin Jiang. 2023. WizardCoder: Empowering code large language models with evol-instruct. arXiv preprint arXiv:2306.08568 (2023).","journal-title":"arXiv preprint arXiv:2306.08568"},{"key":"e_1_3_3_166_2","first-page":"649","volume-title":"ICML","author":"Maddison Chris","year":"2014","unstructured":"Chris Maddison and Daniel Tarlow. 2014. Structured generative models of natural source code. In ICML. 649\u2013657."},{"key":"e_1_3_3_167_2","first-page":"304","volume-title":"ICSE","author":"Malik Rabee Sohail","year":"2019","unstructured":"Rabee Sohail Malik, Jibesh Patra, and Michael Pradel. 2019. NL2Type: Inferring JavaScript function types from natural language information. In ICSE. 304\u2013315."},{"key":"e_1_3_3_168_2","first-page":"336","volume-title":"ICSE","author":"Mastropaolo Antonio","year":"2021","unstructured":"Antonio Mastropaolo, Simone Scalabrino, Nathan Cooper, David Nader Palacio, Denys Poshyvanyk, et\u00a0al. 2021. Studying the usage of text-to-text transfer transformer to support code-related tasks. In ICSE. 336\u2013347."},{"key":"e_1_3_3_169_2","article-title":"Modeling functional similarity in source code with graph-based Siamese networks","author":"Mehrotra Nikita","year":"2021","unstructured":"Nikita Mehrotra, Navdha Agarwal, Piyush Gupta, Saket Anand, David Lo, and Rahul Purandare. 2021. Modeling functional similarity in source code with graph-based Siamese networks. TSE (2021).","journal-title":"TSE"},{"key":"e_1_3_3_170_2","first-page":"925","volume-title":"ESEC\/FSE","author":"Mesbah Ali","year":"2019","unstructured":"Ali Mesbah, Andrew Rice, Emily Johnston, Nick Glorioso, and Edward Aftandilian. 2019. DeepDelta: Learning to repair compilation errors. In ESEC\/FSE. 925\u2013936."},{"key":"e_1_3_3_171_2","volume-title":"ICLR","author":"Mikolov Tom\u00e1s","year":"2013","unstructured":"Tom\u00e1s Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. In ICLR."},{"key":"e_1_3_3_172_2","first-page":"2241","volume-title":"ICSE","author":"Mir Amir M.","year":"2022","unstructured":"Amir M. Mir, Evaldas Latoskinas, Sebastian Proksch, and Georgios Gousios. 2022. Type4Py: Practical deep similarity learning-based type inference for Python. In ICSE. 2241\u20132252."},{"key":"e_1_3_3_173_2","first-page":"880","volume-title":"ICSE","author":"Moreno Laura","year":"2015","unstructured":"Laura Moreno, Gabriele Bavota, Massimiliano Di Penta, Rocco Oliveto, and Andrian Marcus. 2015. How can I use this method?. In ICSE, Vol. 1. 880\u2013890."},{"key":"e_1_3_3_174_2","volume-title":"AAAI","author":"Mou Lili","year":"2016","unstructured":"Lili Mou, Ge Li, Lu Zhang, Tao Wang, and Zhi Jin. 2016. Convolutional neural networks over tree structures for programming language processing. In AAAI, Vol. 30."},{"key":"e_1_3_3_175_2","first-page":"14:1\u201314:12","volume-title":"ASE","author":"Mu Fangwen","year":"2022","unstructured":"Fangwen Mu, Xiao Chen, Lin Shi, Song Wang, and Qing Wang. 2022. Automatic comment generation via multi-pass deliberation. In ASE. ACM, 14:1\u201314:12."},{"key":"e_1_3_3_176_2","first-page":"1026","volume-title":"ASE","author":"Nafi Kawser Wazed","year":"2019","unstructured":"Kawser Wazed Nafi, Tonny Shekha Kar, Banani Roy, Chanchal K. Roy, and Kevin A. Schneider. 2019. CLCDSA: Cross language code clone detection using syntactical features and API documentation. In ASE. 1026\u20131037."},{"key":"e_1_3_3_177_2","first-page":"1","volume-title":"ESEM","author":"Nair Aravind","year":"2020","unstructured":"Aravind Nair, Avijit Roy, and Karl Meinke. 2020. funcGNN: A graph neural network approach to program similarity. In ESEM. 1\u201311."},{"key":"e_1_3_3_178_2","first-page":"75","volume-title":"ESEC\/FSE","author":"Nan Zifan","year":"2020","unstructured":"Zifan Nan, Hui Guan, and Xipeng Shen. 2020. HISyn: Human learning-inspired natural language programming. In ESEC\/FSE. 75\u201386."},{"key":"e_1_3_3_179_2","first-page":"253","volume-title":"ASE","author":"Nguyen Phuong T.","year":"2021","unstructured":"Phuong T. Nguyen, Claudio Di Sipio, Juri Di Rocco, Massimiliano Di Penta, and Davide Di Ruscio. 2021. Adversarial attacks to API recommender systems: Time to wake up and smell the coffee?. In ASE. 253\u2013265."},{"key":"e_1_3_3_180_2","first-page":"1372","volume-title":"ICSE","author":"Nguyen Son","year":"2020","unstructured":"Son Nguyen, Hung Phan, Trinh Le, and Tien N. Nguyen. 2020. Suggesting natural method names to check name consistencies. In ICSE. 1372\u20131384."},{"key":"e_1_3_3_181_2","first-page":"438","volume-title":"ICSE","author":"Nguyen Trong Duc","year":"2017","unstructured":"Trong Duc Nguyen, Anh Tuan Nguyen, Hung Dang Phan, and Tien N. Nguyen. 2017. Exploring API embedding for API usages and applications. In ICSE. 438\u2013449."},{"key":"e_1_3_3_182_2","volume-title":"ICLR","author":"Nijkamp Erik","year":"2023","unstructured":"Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2023. CodeGen: An open large language model for code with multi-turn program synthesis. In ICLR."},{"key":"e_1_3_3_183_2","first-page":"1","volume-title":"ICSE","author":"Niu Changan","year":"2022","unstructured":"Changan Niu, Chuanyi Li, Vincent Ng, Jidong Ge, Liguo Huang, and Bin Luo. 2022. SPT-Code: Sequence-to-sequence pre-training for learning source code representations. In ICSE. 1\u201313."},{"key":"e_1_3_3_184_2","first-page":"4861","volume-title":"ICML","author":"Nye Maxwell","year":"2019","unstructured":"Maxwell Nye, Luke Hewitt, Joshua Tenenbaum, and Armando Solar-Lezama. 2019. Learning to infer program sketches. In ICML. 4861\u20134870."},{"key":"e_1_3_3_185_2","unstructured":"OpenAI. 2022. ChatGPT. https:\/\/openai.com\/blog\/chatgpt\/. (2022)."},{"key":"e_1_3_3_186_2","unstructured":"OpenAI. 2023. ChatGPT. https:\/\/openai.com\/research\/gpt-4. (2023)."},{"key":"e_1_3_3_187_2","volume-title":"NAACL-HLT: Demonstrations","author":"Ott Myle","year":"2019","unstructured":"Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019. fairseq: A fast, extensible toolkit for sequence modeling. In NAACL-HLT: Demonstrations."},{"key":"e_1_3_3_188_2","article-title":"OptTyper: Probabilistic type inference by optimising logical and natural constraints","author":"Pandi Irene Vlassi","year":"2020","unstructured":"Irene Vlassi Pandi, Earl T. Barr, Andrew D. Gordon, and Charles Sutton. 2020. OptTyper: Probabilistic type inference by optimising logical and natural constraints. arXiv:2004.00348 (2020).","journal-title":"arXiv:2004.00348"},{"key":"e_1_3_3_189_2","first-page":"427","volume-title":"AAAI","author":"Panthaplackel Sheena","year":"2021","unstructured":"Sheena Panthaplackel, Junyi Jessy Li, Milos Gligoric, and Raymond J. Mooney. 2021. Deep just-in-time inconsistency detection between comments and source code. In AAAI, Vol. 35. 427\u2013435."},{"key":"e_1_3_3_190_2","first-page":"1853","volume-title":"ACL","author":"Panthaplackel Sheena","year":"2020","unstructured":"Sheena Panthaplackel, Pengyu Nie, Milos Gligoric, Junyi Jessy Li, and Raymond Mooney. 2020. Learning to update natural language comments based on code changes. In ACL. 1853\u20131868."},{"key":"e_1_3_3_191_2","first-page":"8476","volume-title":"ICML","author":"Peng Dinglan","year":"2021","unstructured":"Dinglan Peng, Shuxin Zheng, Yatao Li, Guolin Ke, Di He, and Tie-Yan Liu. 2021. How could neural networks understand programs?. In ICML, Vol. 139. 8476\u20138486."},{"key":"e_1_3_3_192_2","volume-title":"ICLR","author":"Poesia Gabriel","year":"2022","unstructured":"Gabriel Poesia, Alex Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, and Sumit Gulwani. 2022. Synchromesh: Reliable code generation from pre-trained language models. In ICLR."},{"key":"e_1_3_3_193_2","first-page":"407","volume-title":"ASE","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 ASE. 407\u2013418."},{"key":"e_1_3_3_194_2","first-page":"209","volume-title":"ESEC\/FSE","author":"Pradel Michael","year":"2020","unstructured":"Michael Pradel, Georgios Gousios, Jason Liu, and Satish Chandra. 2020. Typewriter: Neural type prediction with search-based validation. In ESEC\/FSE. 209\u2013220."},{"key":"e_1_3_3_195_2","first-page":"1","article-title":"DeepBugs: A learning approach to name-based bug detection","volume":"2","author":"Pradel Michael","year":"2018","unstructured":"Michael Pradel and Koushik Sen. 2018. DeepBugs: A learning approach to name-based bug detection. OOPSLA 2 (2018), 1\u201325.","journal-title":"OOPSLA"},{"key":"e_1_3_3_196_2","first-page":"479","volume-title":"USENIX Security 19","author":"Quiring Erwin","year":"2019","unstructured":"Erwin Quiring, Alwin Maier, and Konrad Rieck. 2019. Misleading authorship attribution of source code using adversarial learning. In USENIX Security 19. 479\u2013496."},{"key":"e_1_3_3_197_2","first-page":"441","volume-title":"ESEC\/FSE","author":"Rabin Md. Rafiqul Islam","year":"2021","unstructured":"Md. Rafiqul Islam Rabin, Vincent J. Hellendoorn, and Mohammad Amin Alipour. 2021. Understanding neural code intelligence through program simplification. In ESEC\/FSE. ACM, 441\u2013452."},{"key":"e_1_3_3_198_2","doi-asserted-by":"crossref","first-page":"107066","DOI":"10.1016\/j.infsof.2022.107066","article-title":"Memorization and generalization in neural code intelligence models","volume":"153","author":"Rabin Md. Rafiqul Islam","year":"2023","unstructured":"Md. Rafiqul Islam Rabin, Aftab Hussain, Mohammad Amin Alipour, and Vincent J. Hellendoorn. 2023. Memorization and generalization in neural code intelligence models. Information and Software Technology 153 (2023), 107066.","journal-title":"Information and Software Technology"},{"key":"e_1_3_3_199_2","first-page":"1139","volume-title":"ACL","author":"Rabinovich Maxim","year":"2017","unstructured":"Maxim Rabinovich, Mitchell Stern, and Dan Klein. 2017. Abstract syntax networks for code generation and semantic parsing. In ACL. 1139\u20131149."},{"key":"e_1_3_3_200_2","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, et\u00a0al. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. JMLR 21 (2020), 1\u201367.","journal-title":"JMLR"},{"key":"e_1_3_3_201_2","first-page":"2892","volume-title":"ICPR","author":"Ramakrishnan Goutham","year":"2022","unstructured":"Goutham Ramakrishnan and Aws Albarghouthi. 2022. Backdoors in neural models of source code. In ICPR. IEEE, 2892\u20132899."},{"key":"e_1_3_3_202_2","article-title":"Semantic robustness of models of source code","author":"Ramakrishnan Goutham","year":"2020","unstructured":"Goutham Ramakrishnan, Jordan Henkel, Zi Wang, Aws Albarghouthi, Somesh Jha, and Thomas Reps. 2020. Semantic robustness of models of source code. arXiv:2002.03043 (2020).","journal-title":"arXiv:2002.03043"},{"issue":"10","key":"e_1_3_3_203_2","doi-asserted-by":"crossref","first-page":"731","DOI":"10.1145\/3022671.2984041","article-title":"Probabilistic model for code with decision trees","volume":"51","author":"Raychev Veselin","year":"2016","unstructured":"Veselin Raychev, Pavol Bielik, and Martin Vechev. 2016. Probabilistic model for code with decision trees. ACM SIGPLAN Notices 51, 10 (2016), 731\u2013747.","journal-title":"ACM SIGPLAN Notices"},{"key":"e_1_3_3_204_2","first-page":"419","volume-title":"ICPC","author":"Raychev Veselin","year":"2014","unstructured":"Veselin Raychev, Martin Vechev, and Eran Yahav. 2014. Code completion with statistical language models. In ICPC. 419\u2013428."},{"key":"e_1_3_3_205_2","article-title":"Code Llama: Open foundation models for code","author":"Roziere Baptiste","year":"2023","unstructured":"Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, J\u00e9r\u00e9my Rapin, et\u00a0al. 2023. Code Llama: Open foundation models for code. arXiv preprint arXiv:2308.12950 (2023).","journal-title":"arXiv preprint arXiv:2308.12950"},{"key":"e_1_3_3_206_2","volume-title":"NeurIPS","author":"Rozi\u00e8re Baptiste","year":"2020","unstructured":"Baptiste Rozi\u00e8re, Marie-Anne Lachaux, Lowik Chanussot, and Guillaume Lample. 2020. Unsupervised translation of programming languages. In NeurIPS."},{"key":"e_1_3_3_207_2","volume-title":"ICLR","author":"Rozi\u00e8re Baptiste","year":"2022","unstructured":"Baptiste Rozi\u00e8re, Jie Zhang, Fran\u00e7ois Charton, Mark Harman, Gabriel Synnaeve, and Guillaume Lample. 2022. Leveraging automated unit tests for unsupervised code translation. In ICLR."},{"key":"e_1_3_3_208_2","article-title":"Faster than C: Static type inference with Starkiller","volume":"3","author":"Salib Michael","year":"2004","unstructured":"Michael Salib. 2004. Faster than C: Static type inference with Starkiller. PyCon Proceedings, Washington DC 3 (2004).","journal-title":"PyCon Proceedings, Washington DC"},{"key":"e_1_3_3_209_2","first-page":"311","volume-title":"SANER","author":"Santos Eddie Antonio","year":"2018","unstructured":"Eddie Antonio Santos, Joshua Charles Campbell, Dhvani Patel, Abram Hindle, and Jos\u00e9 Nelson Amaral. 2018. Syntax and sensibility: Using language models to detect and correct syntax errors. In SANER. 311\u2013322."},{"key":"e_1_3_3_210_2","volume-title":"USENIX Security","author":"Schuster Roei","year":"2021","unstructured":"Roei Schuster, Congzheng Song, Eran Tromer, and Vitaly Shmatikov. 2021. You autocomplete me: Poisoning vulnerabilities in neural code completion. In USENIX Security."},{"key":"e_1_3_3_211_2","volume-title":"USENIX Security","author":"Severi Giorgio","year":"2021","unstructured":"Giorgio Severi, Jim Meyer, Scott Coull, and Alina Oprea. 2021. Explanation-guided backdoor poisoning attacks against malware classifiers. In USENIX Security."},{"key":"e_1_3_3_212_2","first-page":"411","volume-title":"ICPC","author":"Shahbazi Ramin","year":"2021","unstructured":"Ramin Shahbazi, Rishab Sharma, and Fatemeh H. Fard. 2021. API2Com: On the improvement of automatically generated code comments using API documentations. In ICPC. IEEE, 411\u2013421."},{"key":"e_1_3_3_213_2","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1145\/3524610.3527921","volume-title":"ICPC","author":"Sharma Rishab","year":"2022","unstructured":"Rishab Sharma, Fuxiang Chen, Fatemeh H. Fard, and David Lo. 2022. An exploratory study on code attention in BERT. In ICPC. ACM, 437\u2013448."},{"key":"e_1_3_3_214_2","first-page":"4053","volume-title":"EMNLP","author":"Shi Ensheng","year":"2021","unstructured":"Ensheng Shi, Yanlin Wang, Lun Du, Hongyu Zhang, Shi Han, et\u00a0al. 2021. CAST: Enhancing code summarization with hierarchical splitting and reconstruction of abstract syntax trees. In EMNLP. 4053\u20134062."},{"key":"e_1_3_3_215_2","first-page":"24:1\u201324:12","volume-title":"ASE","author":"Shi Jieke","year":"2022","unstructured":"Jieke Shi, Zhou Yang, Bowen Xu, Hong Jin Kang, and David Lo. 2022. Compressing pre-trained models of code into 3 MB. In ASE. ACM, 24:1\u201324:12."},{"key":"e_1_3_3_216_2","first-page":"107","volume-title":"ESEC\/FSE","author":"Shi Lin","year":"2022","unstructured":"Lin Shi, Fangwen Mu, Xiao Chen, Song Wang, Junjie Wang, Ye Yang, Ge Li, Xin Xia, and Qing Wang. 2022. Are we building on the rock? On the importance of data preprocessing for code summarization. In ESEC\/FSE. ACM, 107\u2013119."},{"key":"e_1_3_3_217_2","first-page":"722","volume-title":"ESEC\/FSE","author":"Shi Yucen","year":"2022","unstructured":"Yucen Shi, Ying Yin, Zhengkui Wang, David Lo, Tao Zhang, Xin Xia, Yuhai Zhao, and Bowen Xu. 2022. How to better utilize code graphs in semantic code search?. In ESEC\/FSE. 722\u2013733."},{"key":"e_1_3_3_218_2","first-page":"3","volume-title":"S&P","author":"Shokri Reza","year":"2017","unstructured":"Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017. Membership inference attacks against machine learning models. In S&P. 3\u201318."},{"key":"e_1_3_3_219_2","article-title":"On-the-fly adaptation of source code models using meta-learning","author":"Shrivastava Disha","year":"2020","unstructured":"Disha Shrivastava, Hugo Larochelle, and Daniel Tarlow. 2020. On-the-fly adaptation of source code models using meta-learning. arXiv:2003.11768 (2020).","journal-title":"arXiv:2003.11768"},{"key":"e_1_3_3_220_2","first-page":"1539","volume-title":"AAAI","author":"Shu Chengxun","year":"2017","unstructured":"Chengxun Shu and Hongyu Zhang. 2017. Neural programming by example. In AAAI. 1539\u20131545."},{"key":"e_1_3_3_221_2","first-page":"1","article-title":"Flow2Vec: Value-flow-based precise code embedding","volume":"4","author":"Sui Yulei","year":"2020","unstructured":"Yulei Sui, Xiao Cheng, Guanqin Zhang, and Haoyu Wang. 2020. Flow2Vec: Value-flow-based precise code embedding. OOPSLA 4 (2020), 1\u201327.","journal-title":"OOPSLA"},{"key":"e_1_3_3_222_2","first-page":"265","volume-title":"Proceedings of the 25th International Conference on Compiler Construction","author":"Sui Yulei","year":"2016","unstructured":"Yulei Sui and Jingling Xue. 2016. SVF: Interprocedural static value-flow analysis in LLVM. In Proceedings of the 25th International Conference on Compiler Construction. 265\u2013266."},{"key":"e_1_3_3_223_2","first-page":"388","volume-title":"ICSE","author":"Sun Weisong","year":"2022","unstructured":"Weisong Sun, Chunrong Fang, Yuchen Chen, Guanhong Tao, Tingxu Han, and Quanjun Zhang. 2022. Code search based on context-aware code translation. In ICSE. 388\u2013400."},{"issue":"12","key":"e_1_3_3_224_2","doi-asserted-by":"crossref","first-page":"3807","DOI":"10.14778\/3554821.3554901","article-title":"Heterogeneous information networks: The past, the present, and the future","volume":"15","author":"Sun Yizhou","year":"2022","unstructured":"Yizhou Sun, Jiawei Han, Xifeng Yan, Philip S. Yu, and Tianyi Wu. 2022. Heterogeneous information networks: The past, the present, and the future. Proc. VLDB Endow. 15, 12 (2022), 3807\u20133811.","journal-title":"Proc. VLDB Endow."},{"key":"e_1_3_3_225_2","first-page":"1609","volume-title":"ICSE","author":"Sun Zhensu","year":"2022","unstructured":"Zhensu Sun, Li Li, Yan Liu, Xiaoning Du, and Li Li. 2022. On the importance of building high-quality training datasets for neural code search. In ICSE. ACM, 1609\u20131620."},{"key":"e_1_3_3_226_2","first-page":"7055","volume-title":"AAAI","author":"Sun Zeyu","year":"2019","unstructured":"Zeyu Sun, Qihao Zhu, Lili Mou, Yingfei Xiong, Ge Li, and Lu Zhang. 2019. A grammar-based structural CNN decoder for code generation. In AAAI, Vol. 33. 7055\u20137062."},{"key":"e_1_3_3_227_2","first-page":"8984","volume-title":"AAAI","author":"Sun Zeyu","year":"2020","unstructured":"Zeyu Sun, Qihao Zhu, Yingfei Xiong, Yican Sun, Lili Mou, and Lu Zhang. 2020. TreeGen: A tree-based transformer architecture for code generation. In AAAI. 8984\u20138991."},{"key":"e_1_3_3_228_2","first-page":"1433","volume-title":"ESEC\/FSE","author":"Svyatkovskiy Alexey","year":"2020","unstructured":"Alexey Svyatkovskiy, Shao Kun Deng, Shengyu Fu, and Neel Sundaresan. 2020. Intellicode compose: Code generation using transformer. In ESEC\/FSE. 1433\u20131443."},{"key":"e_1_3_3_229_2","first-page":"329","volume-title":"MSR","author":"Svyatkovskiy Alexey","year":"2021","unstructured":"Alexey Svyatkovskiy, Sebastian Lee, Anna Hadjitofi, Maik Riechert, Juliana Vicente Franco, and Miltiadis Allamanis. 2021. Fast and memory-efficient neural code completion. In MSR. 329\u2013340."},{"key":"e_1_3_3_230_2","first-page":"2727","volume-title":"SIGKDD","author":"Svyatkovskiy Alexey","year":"2019","unstructured":"Alexey Svyatkovskiy, Ying Zhao, Shengyu Fu, and Neel Sundaresan. 2019. Pythia: Ai-assisted code completion system. In SIGKDD. 2727\u20132735."},{"key":"e_1_3_3_231_2","volume-title":"ICSE","author":"Tang Ze","year":"2022","unstructured":"Ze Tang, Xiaoyu Shen, Chuanyi Li, Jidong Ge, Liguo Huang, Zheling Zhu, and Bin Luo. 2022. AST-Trans: Code summarization with efficient tree-structured attention. In ICSE."},{"key":"e_1_3_3_232_2","first-page":"413","volume-title":"ICPC","author":"Tao Chenning","year":"2022","unstructured":"Chenning Tao, Qi Zhan, Xing Hu, and Xin Xia. 2022. C4: Contrastive cross-language code clone detection. In ICPC. ACM, 413\u2013424."},{"key":"e_1_3_3_233_2","first-page":"19","volume-title":"ICSE Workshops","author":"Tarlow Daniel","year":"2020","unstructured":"Daniel Tarlow, Subhodeep Moitra, Andrew Rice, Zimin Chen, Pierre-Antoine Manzagol, Charles Sutton, and Edward Aftandilian. 2020. Learning to fix build errors with graph2diff neural networks. In ICSE Workshops. 19\u201320."},{"key":"e_1_3_3_234_2","first-page":"981","volume-title":"ASE","author":"Tian Haoye","year":"2020","unstructured":"Haoye Tian, Kui Liu, Abdoul Kader Kabor\u00e9, Anil Koyuncu, Li Li, et\u00a0al. 2020. Evaluating representation learning of code changes for predicting patch correctness in program repair. In ASE. 981\u2013992."},{"key":"e_1_3_3_235_2","first-page":"25","volume-title":"ICSE","author":"Tufano Michele","year":"2019","unstructured":"Michele Tufano, Jevgenija Pantiuchina, Cody Watson, Gabriele Bavota, and Denys Poshyvanyk. 2019. On learning meaningful code changes via neural machine translation. In ICSE. 25\u201336."},{"key":"e_1_3_3_236_2","first-page":"832","volume-title":"ASE","author":"Tufano Michele","year":"2018","unstructured":"Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, et\u00a0al. 2018. An empirical investigation into learning bug-fixing patches in the wild via neural machine translation. In ASE. 832\u2013837."},{"key":"e_1_3_3_237_2","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1145\/3196398.3196431","volume-title":"MSR","author":"Tufano Michele","year":"2018","unstructured":"Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, and Denys Poshyvanyk. 2018. Deep learning similarities from different representations of source code. In MSR. 542\u2013553."},{"key":"e_1_3_3_238_2","volume-title":"ICLR","author":"Vasic Marko","year":"2018","unstructured":"Marko Vasic, Aditya Kanade, Petros Maniatis, David Bieber, and Rishabh Singh. 2018. Neural program repair by jointly learning to localize and repair. In ICLR."},{"issue":"4","key":"e_1_3_3_239_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3418463","article-title":"IR2Vec: LLVM IR based scalable program embeddings","volume":"17","author":"VenkataKeerthy S.","year":"2020","unstructured":"S. VenkataKeerthy, Rohit Aggarwal, Shalini Jain, Maunendra Sankar Desarkar, Ramakrishna Upadrasta, and Y. N. Srikant. 2020. IR2Vec: LLVM IR based scalable program embeddings. TACO 17, 4 (2020), 1\u201327.","journal-title":"TACO"},{"key":"e_1_3_3_240_2","volume-title":"ICSE, Companion Volume","author":"Wan Yao","year":"2022","unstructured":"Yao Wan, Yang He, Zhangqian Bi, Jianguo Zhang, Yulei Sui, Hongyu Zhang, et\u00a0al. 2022. NaturalCC: An open-source toolkit for code intelligence. In ICSE, Companion Volume."},{"key":"e_1_3_3_241_2","first-page":"13","volume-title":"ASE","author":"Wan Yao","year":"2019","unstructured":"Yao Wan, Jingdong Shu, Yulei Sui, Guandong Xu, Zhou Zhao, Jian Wu, and Philip S. Yu. 2019. Multi-modal attention network learning for semantic source code retrieval. In ASE. 13\u201325."},{"key":"e_1_3_3_242_2","first-page":"1233","volume-title":"ESEC\/FSE","author":"Wan Yao","year":"2022","unstructured":"Yao Wan, Shijie Zhang, Hongyu Zhang, Yulei Sui, Guandong Xu, Dezhong Yao, Hai Jin, and Lichao Sun. 2022. You see what I want you to see: Poisoning vulnerabilities in neural code search. In ESEC\/FSE. 1233\u20131245."},{"key":"e_1_3_3_243_2","first-page":"2377","volume-title":"ICSE","author":"Wan Yao","year":"2022","unstructured":"Yao Wan, Wei Zhao, Hongyu Zhang, Yulei Sui, Guandong Xu, and Hai Jin. 2022. What do they capture? - A structural analysis of pre-trained language models for source code. In ICSE. 2377\u20132388."},{"key":"e_1_3_3_244_2","first-page":"397","volume-title":"ASE","author":"Wan Yao","year":"2018","unstructured":"Yao Wan, Zhou Zhao, Min Yang, Guandong Xu, Haochao Ying, Jian Wu, and Philip S. Yu. 2018. Improving automatic source code summarization via deep reinforcement learning. In ASE. 397\u2013407."},{"key":"e_1_3_3_245_2","article-title":"Grammar prompting for domain-specific language generation with large language models","volume":"36","author":"Wang Bailin","year":"2024","unstructured":"Bailin Wang, Zi Wang, Xuezhi Wang, Yuan Cao, Rif A. Saurous, and Yoon Kim. 2024. Grammar prompting for domain-specific language generation with large language models. NeurIPS 36 (2024).","journal-title":"NeurIPS"},{"key":"e_1_3_3_246_2","first-page":"382","volume-title":"ESEC\/FSE","author":"Wang Chaozheng","year":"2022","unstructured":"Chaozheng Wang, Yuanhang Yang, Cuiyun Gao, Yun Peng, Hongyu Zhang, and Michael R. Lyu. 2022. No more fine-tuning? An experimental evaluation of prompt tuning in code intelligence. In ESEC\/FSE. 382\u2013394."},{"key":"e_1_3_3_247_2","first-page":"287","volume-title":"ICSE","author":"Wang Deze","year":"2022","unstructured":"Deze Wang, Zhouyang Jia, Shanshan Li, Yue Yu, Yun Xiong, Wei Dong, and Xiangke Liao. 2022. Bridging pre-trained models and downstream tasks for source code understanding. In ICSE. 287\u2013298."},{"key":"e_1_3_3_248_2","first-page":"1943","article-title":"Combining graph-based learning with automated data collection for code vulnerability detection","volume":"16","author":"Wang Huanting","year":"2020","unstructured":"Huanting Wang, Guixin Ye, Zhanyong Tang, Shin Hwei Tan, et\u00a0al. 2020. Combining graph-based learning with automated data collection for code vulnerability detection. TIFS 16 (2020), 1943\u20131958.","journal-title":"TIFS"},{"key":"e_1_3_3_249_2","article-title":"Synergy between machine\/deep learning and software engineering: How far are we?","author":"Wang Simin","year":"2020","unstructured":"Simin Wang, Liguo Huang, Jidong Ge, Tengfei Zhang, Haitao Feng, Ming Li, He Zhang, and Vincent Ng. 2020. Synergy between machine\/deep learning and software engineering: How far are we? arXiv:2008.05515 (2020).","journal-title":"arXiv:2008.05515"},{"key":"e_1_3_3_250_2","first-page":"297","volume-title":"ICSE","author":"Wang Song","year":"2016","unstructured":"Song Wang, Taiyue Liu, and Lin Tan. 2016. Automatically learning semantic features for defect prediction. In ICSE. 297\u2013308."},{"key":"e_1_3_3_251_2","first-page":"261","volume-title":"SANER","author":"Wang Wenhan","year":"2020","unstructured":"Wenhan Wang, Ge Li, Bo Ma, Xin Xia, and Zhi Jin. 2020. Detecting code clones with graph neural network and flow-augmented abstract syntax tree. In SANER. 261\u2013271."},{"key":"e_1_3_3_252_2","article-title":"SynCoBERT: Syntax-guided multi-modal contrastive pre-training for code representation","author":"Wang Xin","year":"2021","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:2108.04556 (2021).","journal-title":"arXiv:2108.04556"},{"key":"e_1_3_3_253_2","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1145\/3524610.3527903","volume-title":"ICPC","author":"Wang Yu","year":"2022","unstructured":"Yu Wang, Yu Dong, Xuesong Lu, and Aoying Zhou. 2022. GypSum: Learning hybrid representations for code summarization. In ICPC. ACM, 12\u201323."},{"key":"e_1_3_3_254_2","first-page":"1069","volume-title":"EMNLP","author":"Wang Yue","year":"2023","unstructured":"Yue Wang, Hung Le, Akhilesh Gotmare, Nghi D. Q. Bui, Junnan Li, and Steven C. H. Hoi. 2023. CodeT5+: Open code large language models for code understanding and generation. In EMNLP. 1069\u20131088."},{"key":"e_1_3_3_255_2","first-page":"14015","volume-title":"AAAI","author":"Wang Yanlin","year":"2021","unstructured":"Yanlin Wang and Hui Li. 2021. Code completion by modeling flattened abstract syntax trees as graphs. In AAAI, Vol. 35. 14015\u201314023."},{"key":"e_1_3_3_256_2","first-page":"8696","volume-title":"EMNLP","author":"Wang Yue","year":"2021","unstructured":"Yue Wang, Weishi Wang, Shafiq R. Joty, and Steven C. H. Hoi. 2021. CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation. In EMNLP. 8696\u20138708."},{"key":"e_1_3_3_257_2","article-title":"A systematic literature review on the use of deep learning in software engineering research","author":"Watson Cody","year":"2020","unstructured":"Cody Watson, Nathan Cooper, David Nader Palacio, Kevin Moran, and Denys Poshyvanyk. 2020. A systematic literature review on the use of deep learning in software engineering research. arXiv:2009.06520 (2020).","journal-title":"arXiv:2009.06520"},{"key":"e_1_3_3_258_2","first-page":"6559","volume-title":"NeurIPS","author":"Wei Bolin","year":"2019","unstructured":"Bolin Wei, Ge Li, Xin Xia, Zhiyi Fu, and Zhi Jin. 2019. Code generation as a dual task of code summarization. In NeurIPS. 6559\u20136569."},{"key":"e_1_3_3_259_2","first-page":"349","volume-title":"ASE","author":"Wei Bolin","year":"2020","unstructured":"Bolin Wei, Yongmin Li, Ge Li, Xin Xia, and Zhi Jin. 2020. Retrieve and refine: Exemplar-based neural comment generation. In ASE. 349\u2013360."},{"key":"e_1_3_3_260_2","first-page":"3034","volume-title":"IJCAI","author":"Wei Huihui","year":"2017","unstructured":"Huihui Wei and Ming Li. 2017. Supervised deep features for software functional clone detection by exploiting lexical and syntactical information in source code. In IJCAI. 3034\u20133040."},{"key":"e_1_3_3_261_2","volume-title":"ICLR","author":"Wei Jiayi","year":"2020","unstructured":"Jiayi Wei, Maruth Goyal, Greg Durrett, and Isil Dillig. 2020. LambdaNet: Probabilistic type inference using graph neural networks. In ICLR."},{"key":"e_1_3_3_262_2","first-page":"376","volume-title":"ICSE","author":"Wei Moshi","year":"2022","unstructured":"Moshi Wei, Nima Shiri Harzevili, Yuchao Huang, Junjie Wang, and Song Wang. 2022. CLEAR: Contrastive learning for API recommendation. In ICSE. 376\u2013387."},{"key":"e_1_3_3_263_2","first-page":"479","volume-title":"SANER","author":"White Martin","year":"2019","unstructured":"Martin White, Michele Tufano, Matias Martinez, Martin Monperrus, and Denys Poshyvanyk. 2019. Sorting and transforming program repair ingredients via deep learning code similarities. In SANER. 479\u2013490."},{"key":"e_1_3_3_264_2","first-page":"87","volume-title":"ASE","author":"White Martin","year":"2016","unstructured":"Martin White, Michele Tufano, Christopher Vendome, and Denys Poshyvanyk. 2016. Deep learning code fragments for code clone detection. In ASE. 87\u201398."},{"key":"e_1_3_3_265_2","first-page":"334","volume-title":"MSR","author":"White Martin","year":"2015","unstructured":"Martin White, Christopher Vendome, Mario Linares-V\u00e1squez, and Denys Poshyvanyk. 2015. Toward deep learning software repositories. In MSR. 334\u2013345."},{"key":"e_1_3_3_266_2","first-page":"1078","volume-title":"Findings of ACL","author":"Wu Hongqiu","year":"2021","unstructured":"Hongqiu Wu, Hai Zhao, and Min Zhang. 2021. Code summarization with structure-induced transformer. In Findings of ACL. 1078\u20131090."},{"key":"e_1_3_3_267_2","first-page":"34:1\u201334:13","volume-title":"ASE","author":"Wu Yueming","year":"2022","unstructured":"Yueming Wu, Siyue Feng, Deqing Zou, and Hai Jin. 2022. Detecting semantic code clones by building AST-based Markov chains model. In ASE. ACM, 34:1\u201334:13."},{"key":"e_1_3_3_268_2","unstructured":"Yuxin Wu Alexander Kirillov Francisco Massa Wan-Yen Lo and Ross Girshick. 2019. Detectron2. https:\/\/github.com\/facebookresearch\/detectron2. (2019)."},{"key":"e_1_3_3_269_2","first-page":"821","volume-title":"ASE","author":"Wu Yueming","year":"2020","unstructured":"Yueming Wu, Deqing Zou, Shihan Dou, Siru Yang, Wei Yang, Feng Cheng, Hong Liang, and Hai Jin. 2020. SCDetector: Software functional clone detection based on semantic tokens analysis. In ASE. 821\u2013833."},{"key":"e_1_3_3_270_2","first-page":"2365","volume-title":"ICSE","author":"Wu Yueming","year":"2022","unstructured":"Yueming Wu, Deqing Zou, Shihan Dou, Wei Yang, Duo Xu, and Hai Jin. 2022. VulCNN: An image-inspired scalable vulnerability detection system. In ICSE. 2365\u20132376."},{"key":"e_1_3_3_271_2","volume-title":"ICSE","author":"Xia Chunqiu Steven","year":"2023","unstructured":"Chunqiu Steven Xia, Yuxiang Wei, and Lingming Zhang. 2023. Automated program repair in the era of large pre-trained language models. In ICSE."},{"key":"e_1_3_3_272_2","first-page":"68:1\u201368:12","volume-title":"ASE","author":"Xie Rui","year":"2022","unstructured":"Rui Xie, Tianxiang Hu, Wei Ye, and Shikun Zhang. 2022. Low-resources project-specific code summarization. In ASE. ACM, 68:1\u201368:12."},{"key":"e_1_3_3_273_2","first-page":"138","volume-title":"ICPC","author":"Xie Rui","year":"2021","unstructured":"Rui Xie, Wei Ye, Jinan Sun, and Shikun Zhang. 2021. Exploiting method names to improve code summarization: A deliberation multi-task learning approach. In ICPC. IEEE, 138\u2013148."},{"key":"e_1_3_3_274_2","first-page":"6045","volume-title":"ACL","author":"Xu Frank F.","year":"2020","unstructured":"Frank F. Xu, Zhengbao Jiang, Pengcheng Yin, Bogdan Vasilescu, and Graham Neubig. 2020. Incorporating external knowledge through pre-training for natural language to code generation. In ACL. 6045\u20136052."},{"key":"e_1_3_3_275_2","first-page":"590","volume-title":"S&P","author":"Yamaguchi Fabian","year":"2014","unstructured":"Fabian Yamaguchi, Nico Golde, Daniel Arp, and Konrad Rieck. 2014. Modeling and discovering vulnerabilities with code property graphs. In S&P. 590\u2013604."},{"key":"e_1_3_3_276_2","first-page":"361","volume-title":"SANER","author":"Yang Guang","year":"2022","unstructured":"Guang Yang, Xiang Chen, Yanlin Zhou, and Chi Yu. 2022. DualSC: Automatic generation and summarization of shellcode via transformer and dual learning. In SANER. 361\u2013372."},{"issue":"10","key":"e_1_3_3_277_2","first-page":"206","article-title":"A survey on deep learning for software engineering","volume":"54","author":"Yang Yanming","year":"2022","unstructured":"Yanming Yang, Xin Xia, David Lo, and John Grundy. 2022. A survey on deep learning for software engineering. ACM Comput. Surv. 54, 10s, Article 206 (Sep.2022), 73 pages.","journal-title":"ACM Comput. Surv."},{"key":"e_1_3_3_278_2","first-page":"1","volume-title":"ICPC","author":"Yang Zhen","year":"2021","unstructured":"Zhen Yang, Jacky Keung, Xiao Yu, Xiaodong Gu, Zhengyuan Wei, Xiaoxue Ma, and Miao Zhang. 2021. A multi-modal transformer-based code summarization approach for smart contracts. In ICPC. IEEE, 1\u201312."},{"key":"e_1_3_3_279_2","first-page":"1482","volume-title":"ICSE","author":"Yang Zhou","year":"2022","unstructured":"Zhou Yang, Jieke Shi, Junda He, and David Lo. 2022. Natural attack for pre-trained models of code. In ICSE. ACM, 1482\u20131493."},{"key":"e_1_3_3_280_2","first-page":"2203","volume-title":"The World Wide Web Conference","author":"Yao Ziyu","year":"2019","unstructured":"Ziyu Yao, Jayavardhan Reddy Peddamail, and Huan Sun. 2019. CoaCor: Code annotation for code retrieval with reinforcement learning. In The World Wide Web Conference. 2203\u20132214."},{"key":"e_1_3_3_281_2","first-page":"10799","volume-title":"ICML","author":"Yasunaga Michihiro","year":"2020","unstructured":"Michihiro Yasunaga and Percy Liang. 2020. Graph-based, self-supervised program repair from diagnostic feedback. In ICML. 10799\u201310808."},{"key":"e_1_3_3_282_2","doi-asserted-by":"crossref","first-page":"2309","DOI":"10.1145\/3366423.3380295","volume-title":"Proceedings of The Web Conference 2020","author":"Ye Wei","year":"2020","unstructured":"Wei Ye, Rui Xie, Jinglei Zhang, Tianxiang Hu, Xiaoyin Wang, and Shikun Zhang. 2020. Leveraging code generation to improve code retrieval and summarization via dual learning. In Proceedings of The Web Conference 2020. 2309\u20132319."},{"key":"e_1_3_3_283_2","first-page":"1","article-title":"Adversarial examples for models of code","volume":"4","author":"Yefet Noam","year":"2020","unstructured":"Noam Yefet, Uri Alon, and Eran Yahav. 2020. Adversarial examples for models of code. OOPSLA 4 (2020), 1\u201330.","journal-title":"OOPSLA"},{"key":"e_1_3_3_284_2","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1109\/CSF.2018.00027","volume-title":"2018 IEEE 31st Computer Security Foundations Symposium (CSF)","author":"Yeom Samuel","year":"2018","unstructured":"Samuel Yeom, Irene Giacomelli, Matt Fredrikson, and Somesh Jha. 2018. Privacy risk in machine learning: Analyzing the connection to overfitting. In 2018 IEEE 31st Computer Security Foundations Symposium (CSF). IEEE, 268\u2013282."},{"key":"e_1_3_3_285_2","first-page":"440","volume-title":"ACL","author":"Yin Pengcheng","year":"2017","unstructured":"Pengcheng Yin and Graham Neubig. 2017. A syntactic neural model for general-purpose code generation. In ACL. 440\u2013450."},{"key":"e_1_3_3_286_2","first-page":"1","volume-title":"ICSE","author":"Yu Hao","year":"2024","unstructured":"Hao Yu, Bo Shen, Dezhi Ran, Jiaxin Zhang, Qi Zhang, Yuchi Ma, Guangtai Liang, Ying Li, Qianxiang Wang, and Tao Xie. 2024. CoderEval: A benchmark of pragmatic code generation with generative pre-trained models. In ICSE. 1\u201312."},{"key":"e_1_3_3_287_2","first-page":"1653","volume-title":"EMNLP","author":"Yu Tao","year":"2018","unstructured":"Tao Yu, Michihiro Yasunaga, Kai Yang, Rui Zhang, Dongxu Wang, Zifan Li, and Dragomir R. Radev. 2018. SyntaxSQLNet: Syntax tree networks for complex and cross-domain text-to-SQL task. In EMNLP. 1653\u20131663."},{"key":"e_1_3_3_288_2","first-page":"1962","volume-title":"EMNLP","author":"Yu Tao","year":"2019","unstructured":"Tao Yu, Rui Zhang, Heyang Er, Suyi Li, Eric Xue, Bo Pang, Xi Victoria Lin, et\u00a0al. 2019. CoSQL: A conversational text-to-SQL challenge towards cross-domain natural language interfaces to databases. In EMNLP. 1962\u20131979."},{"key":"e_1_3_3_289_2","first-page":"3911","volume-title":"EMNLP","author":"Yu Tao","year":"2018","unstructured":"Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, et\u00a0al. 2018. Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL task. In EMNLP. 3911\u20133921."},{"key":"e_1_3_3_290_2","first-page":"4511","volume-title":"ACL","author":"Yu Tao","year":"2019","unstructured":"Tao Yu, Rui Zhang, Michihiro Yasunaga, Yi Chern Tan, Xi Victoria Lin, Suyi Li, Heyang Er, Irene Li, Bo Pang, Tao Chen, et\u00a0al. 2019. SParC: Cross-domain semantic parsing in context. In ACL. 4511\u20134523."},{"key":"e_1_3_3_291_2","first-page":"1169","volume-title":"AAAI","author":"Zhang Huangzhao","year":"2020","unstructured":"Huangzhao Zhang, Zhuo Li, Ge Li, Lei Ma, Yang Liu, and Zhi Jin. 2020. Generating adversarial examples for holding robustness of source code processing models. In AAAI, Vol. 34. 1169\u20131176."},{"key":"e_1_3_3_292_2","first-page":"4454","volume-title":"Findings of ACL","author":"Zhang Jingfeng","year":"2021","unstructured":"Jingfeng Zhang, Haiwen Hong, Yin Zhang, Yao Wan, Ye Liu, and Yulei Sui. 2021. Disentangled code representation learning for multiple programming languages. In Findings of ACL. 4454\u20134466."},{"key":"e_1_3_3_293_2","first-page":"22:1\u201322:12","volume-title":"37th IEEE\/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022","author":"Zhang Jiyang","year":"2022","unstructured":"Jiyang Zhang, Sheena Panthaplackel, Pengyu Nie, Junyi Jessy Li, and Milos Gligoric. 2022. CoditT5: Pretraining for source code and natural language editing. In 37th IEEE\/ACM International Conference on Automated Software Engineering, ASE 2022, Rochester, MI, USA, October 10-14, 2022. ACM, 22:1\u201322:12."},{"key":"e_1_3_3_294_2","first-page":"1385","volume-title":"ICSE","author":"Zhang Jian","year":"2020","unstructured":"Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, and Xudong Liu. 2020. Retrieval-based neural source code summarization. In ICSE. 1385\u20131397."},{"key":"e_1_3_3_295_2","first-page":"783","volume-title":"ICSE","author":"Zhang Jian","year":"2019","unstructured":"Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, Kaixuan Wang, and Xudong Liu. 2019. A novel neural source code representation based on abstract syntax tree. In ICSE. 783\u2013794."},{"key":"e_1_3_3_296_2","first-page":"1","volume-title":"Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems","author":"Zhang Tianyi","year":"2021","unstructured":"Tianyi Zhang, Zhiyang Chen, Yuanli Zhu, Priyan Vaithilingam, Xinyu Wang, and Elena L. Glassman. 2021. Interpretable program synthesis. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1\u201316."},{"issue":"3","key":"e_1_3_3_297_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3374217","article-title":"Adversarial attacks on deep-learning models in natural language processing: A survey","volume":"11","author":"Zhang Wei Emma","year":"2020","unstructured":"Wei Emma Zhang, Quan Z. Sheng, Ahoud Alhazmi, and Chenliang Li. 2020. Adversarial attacks on deep-learning models in natural language processing: A survey. TIST 11, 3 (2020), 1\u201341.","journal-title":"TIST"},{"key":"e_1_3_3_298_2","first-page":"1073","volume-title":"ESEC\/FSE","author":"Zhang Zhaowei","year":"2022","unstructured":"Zhaowei Zhang, Hongyu Zhang, Beijun Shen, and Xiaodong Gu. 2022. Diet code is healthy: Simplifying programs for pre-trained models of code. In ESEC\/FSE. 1073\u20131084."},{"key":"e_1_3_3_299_2","first-page":"141","volume-title":"ESEC\/FSE","author":"Zhao Gang","year":"2018","unstructured":"Gang Zhao and Jeff Huang. 2018. DeepSim: Deep learning code functional similarity. In ESEC\/FSE. 141\u2013151."},{"key":"e_1_3_3_300_2","article-title":"CodeGeeX: A pre-trained model for code generation with multilingual evaluations on humaneval-x","author":"Zheng Qinkai","year":"2023","unstructured":"Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Zihan Wang, Lei Shen, Andi Wang, Yang Li, et\u00a0al. 2023. CodeGeeX: A pre-trained model for code generation with multilingual evaluations on humaneval-x. arXiv preprint arXiv:2303.17568 (2023).","journal-title":"arXiv preprint arXiv:2303.17568"},{"key":"e_1_3_3_301_2","article-title":"Seq2sql: Generating structured queries from natural language using reinforcement learning","author":"Zhong Victor","year":"2017","unstructured":"Victor Zhong, Caiming Xiong, and Richard Socher. 2017. Seq2sql: Generating structured queries from natural language using reinforcement learning. arXiv:1709.00103 (2017).","journal-title":"arXiv:1709.00103"},{"key":"e_1_3_3_302_2","first-page":"425","volume-title":"ICSME","author":"Zhou Xin","year":"2021","unstructured":"Xin Zhou, DongGyun Han, and David Lo. 2021. Assessing generalizability of CodeBERT. In ICSME. IEEE, 425\u2013436."},{"key":"e_1_3_3_303_2","first-page":"10197","volume-title":"NeurIPS","author":"Zhou Yaqin","year":"2019","unstructured":"Yaqin Zhou, Shangqing Liu, Jing Kai Siow, Xiaoning Du, and Yang Liu. 2019. Devign: Effective vulnerability identification by learning comprehensive program semantics via graph neural networks. In NeurIPS. 10197\u201310207."},{"issue":"4","key":"e_1_3_3_304_2","first-page":"60:1\u201360:30","article-title":"Adversarial robustness of deep code comment generation","volume":"31","author":"Zhou Yu","year":"2022","unstructured":"Yu Zhou, Xiaoqing Zhang, Juanjuan Shen, Tingting Han, Taolue Chen, and Harald C. Gall. 2022. Adversarial robustness of deep code comment generation. TOSEM 31, 4 (2022), 60:1\u201360:30.","journal-title":"TOSEM"},{"key":"e_1_3_3_305_2","first-page":"883","volume-title":"ASE","author":"Zhu Qihao","year":"2020","unstructured":"Qihao Zhu, Zeyu Sun, Xiran Liang, Yingfei Xiong, and Lu Zhang. 2020. OCoR: An overlapping-aware code retriever. In ASE. 883\u2013894."},{"key":"e_1_3_3_306_2","first-page":"341","volume-title":"ESEC\/FSE","author":"Zhu Qihao","year":"2021","unstructured":"Qihao Zhu, Zeyu Sun, Yuan-an Xiao, Wenjie Zhang, Kang Yuan, Yingfei Xiong, and Lu Zhang. 2021. A syntax-guided edit decoder for neural program repair. In ESEC\/FSE. 341\u2013353."},{"key":"e_1_3_3_307_2","first-page":"1089","volume-title":"SANER","author":"Zhu Xiaoning","year":"2022","unstructured":"Xiaoning Zhu, Chaofeng Sha, and Junyu Niu. 2022. A simple retrieval-based method for code comment generation. In SANER. 1089\u20131100."},{"key":"e_1_3_3_308_2","article-title":"\\(\\mu\\) VulDeePecker: A deep learning-based system for multiclass vulnerability detection","author":"Zou Deqing","year":"2019","unstructured":"Deqing Zou, Sujuan Wang, Shouhuai Xu, Zhen Li, and Hai Jin. 2019. \\(\\mu\\) VulDeePecker: A deep learning-based system for multiclass vulnerability detection. TDSC (2019).","journal-title":"TDSC"},{"issue":"2","key":"e_1_3_3_309_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3429444","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. TOSEM 30, 2 (2021), 1\u201331.","journal-title":"TOSEM"},{"key":"e_1_3_3_310_2","volume-title":"ICLR","author":"Z\u00fcgner Daniel","year":"2021","unstructured":"Daniel Z\u00fcgner, Tobias Kirschstein, Michele Catasta, Jure Leskovec, and Stephan G\u00fcnnemann. 2021. Language-agnostic representation learning of source code from structure and context. In ICLR."}],"container-title":["ACM Computing Surveys"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664597","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3664597","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:03:44Z","timestamp":1750291424000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664597"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,3]]},"references-count":309,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2024,12,31]]}},"alternative-id":["10.1145\/3664597"],"URL":"https:\/\/doi.org\/10.1145\/3664597","relation":{},"ISSN":["0360-0300","1557-7341"],"issn-type":[{"value":"0360-0300","type":"print"},{"value":"1557-7341","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,3]]},"assertion":[{"value":"2023-01-19","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-04-22","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}