{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:23:42Z","timestamp":1769732622988,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":39,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T00:00:00Z","timestamp":1712880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Natural Science Foundation of China (NSFC)","award":["62072309"],"award-info":[{"award-number":["62072309"]}]},{"name":"CAS Project for Young Scientists in Basic Research","award":["YSBR-040"],"award-info":[{"award-number":["YSBR-040"]}]},{"name":"ISCAS New Cultivation Project","award":["ISCAS-PYFX-202201"],"award-info":[{"award-number":["ISCAS-PYFX-202201"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,4,12]]},"DOI":"10.1145\/3597503.3639120","type":"proceedings-article","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T16:43:26Z","timestamp":1712940206000},"page":"1-12","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["When Neural Code Completion Models Size up the Situation: Attaining Cheaper and Faster Completion through Dynamic Model Inference"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5393-7858","authenticated-orcid":false,"given":"Zhensu","family":"Sun","sequence":"first","affiliation":[{"name":"Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3728-9541","authenticated-orcid":false,"given":"Xiaoning","family":"Du","sequence":"additional","affiliation":[{"name":"Monash University, Melbourne, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0581-2679","authenticated-orcid":false,"given":"Fu","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China"},{"name":"University of Chinese Academy of Sciences, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1469-2063","authenticated-orcid":false,"given":"Shangwen","family":"Wang","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2990-1614","authenticated-orcid":false,"given":"Li","family":"Li","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2024,4,12]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Retrieved","year":"2022","unstructured":"2022. Code faster with AI completions | TabNine. Retrieved Nov 25, 2022 from https:\/\/www.tabnine.com\/"},{"key":"e_1_3_2_1_2_1","volume-title":"Retrieved","author":"Copilot GitHub","year":"2022","unstructured":"2022. GitHub Copilot \u2022 Your AI pair programmer. Retrieved Nov 25, 2022 from https:\/\/copilot.github.com\/"},{"key":"e_1_3_2_1_3_1","volume-title":"Retrieved","year":"2022","unstructured":"2022. ML-powered coding companion - Amazon CodeWhisperer - Amazon Web Services. Retrieved Nov 25, 2022 from https:\/\/aws.amazon.com\/codewhisperer\/"},{"key":"e_1_3_2_1_4_1","unstructured":"2023. Aaron Mok. Retrieved July 31 2023 from https:\/\/www.businessinsider.com\/how-much-chatgpt-costs-openai-to-run-estimate-report-2023-4"},{"key":"e_1_3_2_1_5_1","volume-title":"Cursor - The AI-first Code Editor","year":"2023","unstructured":"2023. Cursor - The AI-first Code Editor. Retrieved Jul 25, 2023 from https:\/\/www.cursor.so\/"},{"key":"e_1_3_2_1_6_1","unstructured":"2023. SEC. Retrieved January 31 2023 from https:\/\/sites.google.com\/view\/stop-exit-controller"},{"key":"e_1_3_2_1_7_1","unstructured":"Mark Chen Jerry Tworek Heewoo Jun Qiming Yuan Henrique Ponde Jared Kaplan Harrison Edwards Yura 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 David W. Cummings Matthias Plappert Fotios Chantzis Elizabeth Barnes Ariel Herbert-Voss William H. Guss Alex Nichol Igor Babuschkin S. Arun Balaji Shantanu Jain Andrew Carr Jan Leike Joshua Achiam Vedant Misra Evan Morikawa Alec Radford Matthew M. 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. ArXiv abs\/2107.03374 (2021)."},{"key":"e_1_3_2_1_8_1","volume-title":"Reducing the Carbon Impact of Generative AI Inference (today and","author":"Chien Andrew A","year":"2035","unstructured":"Andrew A Chien, Liuzixuan Lin, Hai Nguyen, Varsha Rao, Tristan Sharma, and Rajini Wijayawardana. 2023. Reducing the Carbon Impact of Generative AI Inference (today and in 2035). ACM Hot Carbon 2023 (2023)."},{"key":"e_1_3_2_1_9_1","volume-title":"Sampling techniques","author":"Cochran William G","unstructured":"William G Cochran. 1977. Sampling techniques. Wiley Eastern Limited."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411973"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jss.2023.111741"},{"key":"e_1_3_2_1_12_1","volume-title":"Code-BERT: A Pre-Trained Model for Programming and Natural Languages. ArXiv abs\/2002.08155","author":"Feng Zhangyin","year":"2020","unstructured":"Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou. 2020. Code-BERT: A Pre-Trained Model for Programming and Natural Languages. ArXiv abs\/2002.08155 (2020). https:\/\/api.semanticscholar.org\/CorpusID:211171605"},{"key":"e_1_3_2_1_13_1","volume-title":"DynaBERT: Dynamic BERT with Adaptive Width and Depth. ArXiv abs\/2004.04037","author":"Hou Lu","year":"2020","unstructured":"Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, and Qun Liu. 2020. DynaBERT: Dynamic BERT with Adaptive Width and Depth. ArXiv abs\/2004.04037 (2020)."},{"key":"e_1_3_2_1_14_1","volume-title":"Large language models for software engineering: A systematic literature review. arXiv preprint arXiv:2308.10620","author":"Hou Xinyi","year":"2023","unstructured":"Xinyi Hou, Yanjie Zhao, Yue Liu, Zhou Yang, Kailong Wang, Li Li, Xiapu Luo, David Lo, John Grundy, and Haoyu Wang. 2023. Large language models for software engineering: A systematic literature review. arXiv preprint arXiv:2308.10620 (2023)."},{"key":"e_1_3_2_1_15_1","volume-title":"CodeSearchNet Challenge: Evaluating the State of Semantic Code Search. ArXiv abs\/1909.09436","author":"Husain Hamel","year":"2019","unstructured":"Hamel Husain, Hongqi Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt. 2019. CodeSearchNet Challenge: Evaluating the State of Semantic Code Search. ArXiv abs\/1909.09436 (2019)."},{"key":"e_1_3_2_1_16_1","volume-title":"Tim Verbelen, Bert Vankeirsbilck, Pieter Simoens, and Bart Dhoedt.","author":"Leroux Sam","year":"2017","unstructured":"Sam Leroux, Steven Bohez, Elias De Coninck, Tim Verbelen, Bert Vankeirsbilck, Pieter Simoens, and Bart Dhoedt. 2017. The cascading neural network: building the Internet of Smart Things. Knowledge and Information Systems (2017)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.findings-emnlp.43"},{"key":"e_1_3_2_1_18_1","unstructured":"Raymond Li Loubna Ben Allal Yangtian Zi Niklas Muennighoff Denis Kocetkov Chenghao Mou Marc Marone Christopher Akiki Jia Li Jenny Chim Qian Liu Evgenii Zheltonozhskii Terry Yue Zhuo Thomas Wang Olivier Dehaene Mishig Davaadorj Joel Lamy-Poirier Jo\u00e3o Monteiro Oleh Shliazhko Nicolas Gontier Nicholas Meade Armel Zebaze Ming-Ho Yee Logesh Kumar Umapathi Jian Zhu Benjamin Lipkin Muhtasham Oblokulov Zhiruo Wang Rudra Murthy Jason Stillerman Siva Sankalp Patel Dmitry Abulkhanov Marco Zocca Manan Dey Zhihan Zhang Nour Fahmy Urvashi Bhattacharyya Wenhao Yu Swayam Singh Sasha Luccioni Paulo Villegas Maxim Kunakov Fedor Zhdanov Manuel Romero Tony Lee Nadav Timor Jennifer Ding Claire Schlesinger Hailey Schoelkopf Jan Ebert Tri Dao Mayank Mishra Alex Gu Jennifer Robinson Carolyn Jane Anderson Brendan Dolan-Gavitt Danish Contractor Siva Reddy Daniel Fried Dzmitry Bahdanau Yacine Jernite Carlos Mu\u00f1oz Ferrandis Sean Hughes Thomas Wolf Arjun Guha Leandro von Werra and Harm de Vries. 2023. StarCoder: may the source be with you! (2023). arXiv:2305.06161 [cs.CL]"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.abq1158"},{"key":"e_1_3_2_1_20_1","volume-title":"Finding decision jumps in text classification. Neurocomputing","author":"Liu Xianggen","year":"2020","unstructured":"Xianggen Liu, Lili Mou, Haotian Cui, Zhengdong Lu, and Sen Song. 2020. Finding decision jumps in text classification. Neurocomputing (2020)."},{"key":"e_1_3_2_1_21_1","volume-title":"When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming. ArXiv abs\/2306.04930","author":"Mozannar Hussein","year":"2023","unstructured":"Hussein Mozannar, Gagan Bansal, Adam Fourney, and Eric Horvitz. 2023. When to Show a Suggestion? Integrating Human Feedback in AI-Assisted Programming. ArXiv abs\/2306.04930 (2023). https:\/\/api.semanticscholar.org\/CorpusID:259108906"},{"key":"e_1_3_2_1_22_1","unstructured":"Erik Nijkamp Bo Pang Hiroaki Hayashi Lifu Tu Haiquan Wang Yingbo Zhou Silvio Savarese and Caiming Xiong. 2022. CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis."},{"key":"e_1_3_2_1_23_1","unstructured":"Alec Radford Jeff Wu Rewon Child David Luan Dario Amodei and Ilya Sutskever. 2019. Language Models are Unsupervised Multitask Learners."},{"key":"e_1_3_2_1_24_1","volume-title":"Are Emergent Abilities of Large Language Models a Mirage? ArXiv abs\/2304.15004","author":"Schaeffer Rylan","year":"2023","unstructured":"Rylan Schaeffer, Brando Miranda, and Oluwasanmi Koyejo. 2023. Are Emergent Abilities of Large Language Models a Mirage? ArXiv abs\/2304.15004 (2023). https:\/\/api.semanticscholar.org\/CorpusID:258418299"},{"key":"e_1_3_2_1_25_1","volume-title":"Yi Tay, and Donald Metzler.","author":"Schuster Tal","year":"2022","unstructured":"Tal Schuster, Adam Fisch, Jai Gupta, Mostafa Dehghani, Dara Bahri, Vinh Quang Tran, Yi Tay, and Donald Metzler. 2022. Confident Adaptive Language Modeling. ArXiv abs\/2207.07061 (2022)."},{"key":"e_1_3_2_1_26_1","unstructured":"Bo Shen Jiaxin Zhang Taihong Chen Daoguang Zan Bing Geng An Fu Muhan Zeng Ailun Yu Jichuan Ji Jingyang Zhao Yuenan Guo and Qianxiang Wang. 2023. PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback. arXiv:2307.14936 [cs.CL]"},{"key":"e_1_3_2_1_27_1","volume-title":"Bowen Xu, Junda He, and David Lo.","author":"Shi Jieke","year":"2023","unstructured":"Jieke Shi, Zhou Yang, Hong Jin Kang, Bowen Xu, Junda He, and David Lo. 2023. Towards Smaller, Faster, and Greener Language Models of Code. arXiv e-prints (2023), arXiv-2309."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3551349.3556964"},{"key":"e_1_3_2_1_29_1","unstructured":"Zhensu Sun Xiaoning Du Fu Song Shangwen Wang Mingze Ni and Li Li. 2023. Don't Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems. arXiv:2209.05948 [cs.SE]"},{"key":"e_1_3_2_1_30_1","volume-title":"On the Importance of Building High-quality Training Datasets for Neural Code Search. 2022 IEEE\/ACM 44th International Conference on Software Engineering (ICSE)","author":"Sun Zhensu","year":"2022","unstructured":"Zhensu Sun, Li Li, Y. Liu, and Xiaoning Du. 2022. On the Importance of Building High-quality Training Datasets for Neural Code Search. 2022 IEEE\/ACM 44th International Conference on Software Engineering (ICSE) (2022), 1609--1620."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2016.7900006"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3491101.3519665"},{"key":"e_1_3_2_1_33_1","volume-title":"Proceedings of the Annual Conference on Neural Information Processing Systems. 5998--6008","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In Proceedings of the Annual Conference on Neural Information Processing Systems. 5998--6008."},{"key":"e_1_3_2_1_34_1","volume-title":"Hoi","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. ArXiv abs\/2109.00859 (2021). https:\/\/api.semanticscholar.org\/CorpusID:237386541"},{"key":"e_1_3_2_1_35_1","volume-title":"Lin","author":"Xin Ji","year":"2020","unstructured":"Ji Xin, Raphael Tang, Jaejun Lee, Yaoliang Yu, and Jimmy J. Lin. 2020. DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference. In Annual Meeting of the Association for Computational Linguistics."},{"key":"e_1_3_2_1_36_1","volume-title":"Lin","author":"Xin Ji","year":"2021","unstructured":"Ji Xin, Raphael Tang, Yaoliang Yu, and Jimmy J. Lin. 2021. BERxiT: Early Exiting for BERT with Better Fine-Tuning and Extension to Regression. In Conference of the European Chapter of the Association for Computational Linguistics."},{"key":"e_1_3_2_1_37_1","volume-title":"CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X. ArXiv abs\/2303.17568","author":"Zheng Qinkai","year":"2023","unstructured":"Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shanshan Wang, Yufei Xue, Zi-Yuan Wang, Lei Shen, Andi Wang, Yang Li, Teng Su, Zhilin Yang, and Jie Tang. 2023. CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X. ArXiv abs\/2303.17568 (2023). https:\/\/api.semanticscholar.org\/CorpusID:257834177"},{"key":"e_1_3_2_1_38_1","volume-title":"Proceedings of the Annual Conference on Neural Information Processing Systems","author":"Zhou Wangchunshu","year":"2020","unstructured":"Wangchunshu Zhou, Canwen Xu, Tao Ge, Julian J. McAuley, Ke Xu, and Furu Wei. 2020. BERT Loses Patience: Fast and Robust Inference with Early Exit. In Proceedings of the Annual Conference on Neural Information Processing Systems 2020."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3520312.3534864"}],"event":{"name":"ICSE '24: IEEE\/ACM 46th International Conference on Software Engineering","location":"Lisbon Portugal","acronym":"ICSE '24","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering","IEEE CS","Faculty of Engineering of University of Porto"]},"container-title":["Proceedings of the IEEE\/ACM 46th International Conference on Software Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3597503.3639120","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3597503.3639120","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:49:12Z","timestamp":1750286952000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3597503.3639120"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,12]]},"references-count":39,"alternative-id":["10.1145\/3597503.3639120","10.1145\/3597503"],"URL":"https:\/\/doi.org\/10.1145\/3597503.3639120","relation":{},"subject":[],"published":{"date-parts":[[2024,4,12]]},"assertion":[{"value":"2024-04-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}