{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T04:01:39Z","timestamp":1782878499153,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":68,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T00:00:00Z","timestamp":1726012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,9,11]]},"DOI":"10.1145\/3650212.3680343","type":"proceedings-article","created":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T11:44:25Z","timestamp":1726055065000},"page":"1073-1085","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["When to Stop? Towards Efficient Code Generation in LLMs with Excess Token Prevention"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0943-5049","authenticated-orcid":false,"given":"Lianghong","family":"Guo","sequence":"first","affiliation":[{"name":"Sun Yat-sen University, Zhuhai city, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7761-7269","authenticated-orcid":false,"given":"Yanlin","family":"Wang","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Zhuhai city, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5543-2025","authenticated-orcid":false,"given":"Ensheng","family":"Shi","sequence":"additional","affiliation":[{"name":"Xi'an Jiaotong University, Xi'an city, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2236-228X","authenticated-orcid":false,"given":"Wanjun","family":"Zhong","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou city, China"}],"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 city, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0192-9992","authenticated-orcid":false,"given":"Jiachi","family":"Chen","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Zhuhai city, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8929-628X","authenticated-orcid":false,"given":"Ruikai","family":"Zhang","sequence":"additional","affiliation":[{"name":"Huawei Cloud Computing Technologies, Shenzhen city, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3304-1389","authenticated-orcid":false,"given":"Yuchi","family":"Ma","sequence":"additional","affiliation":[{"name":"Huawei Cloud Computing Technologies, Shenzhen city, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7878-4330","authenticated-orcid":false,"given":"Zibin","family":"Zheng","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Zhuhai city, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,9,11]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Lakshya A Agrawal Aditya Kanade Navin Goyal Shuvendu K Lahiri and Sriram K Rajamani. 2023. Guiding Language Models of Code with Global Context using Monitors. arXiv preprint arXiv:2306.10763."},{"key":"e_1_3_2_1_2_1","unstructured":"Deep Software Analytics. 2024. CodeFast. https:\/\/github.com\/DeepSoftwareAnalytics\/CodeFast"},{"key":"e_1_3_2_1_3_1","volume-title":"Multi-lingual Evaluation of Code Generation Models. In The Eleventh International Conference on Learning Representations.","author":"Athiwaratkun Ben","year":"2022","unstructured":"Ben Athiwaratkun, Sanjay Krishna Gouda, Zijian Wang, Xiaopeng Li, Yuchen Tian, Ming Tan, Wasi Uddin Ahmad, Shiqi Wang, Qing Sun, and Mingyue Shang. 2022. Multi-lingual Evaluation of Code Generation Models. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_1_4_1","unstructured":"Jacob Austin Augustus Odena Maxwell Nye Maarten Bosma Henryk Michalewski David Dohan Ellen Jiang Carrie Cai Michael Terry and Quoc Le. 2021. Program synthesis with large language models. arXiv preprint arXiv:2108.07732."},{"key":"e_1_3_2_1_5_1","volume-title":"Proceedings of the ACM on Software Engineering, 1, FSE","author":"Bairi Ramakrishna","year":"2024","unstructured":"Ramakrishna Bairi, Atharv Sonwane, Aditya Kanade, Arun Iyer, Suresh Parthasarathy, Sriram Rajamani, B Ashok, and Shashank Shet. 2024. Codeplan: Repository-level coding using llms and planning. Proceedings of the ACM on Software Engineering, 1, FSE (2024), 675\u2013698."},{"key":"e_1_3_2_1_6_1","volume-title":"Jun Shern Chan, Samuel R Bowman, Kyunghyun Cho, and Ethan Perez.","author":"Chen Angelica","year":"2023","unstructured":"Angelica Chen, J\u00e9r\u00e9my Scheurer, Tomasz Korbak, Jon Ander Campos, Jun Shern Chan, Samuel R Bowman, Kyunghyun Cho, and Ethan Perez. 2023. Improving code generation by training with natural language feedback. arXiv preprint arXiv:2303.16749."},{"key":"e_1_3_2_1_7_1","volume-title":"Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, and Greg Brockman.","author":"Chen Mark","year":"2021","unstructured":"Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, and Greg Brockman. 2021. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374."},{"key":"e_1_3_2_1_8_1","volume-title":"Tree-to-tree neural networks for program translation. Advances in neural information processing systems, 31","author":"Chen Xinyun","year":"2018","unstructured":"Xinyun Chen, Chang Liu, and Dawn Song. 2018. Tree-to-tree neural networks for program translation. Advances in neural information processing systems, 31 (2018)."},{"key":"e_1_3_2_1_9_1","unstructured":"Tri Dao. 2023. FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning."},{"key":"e_1_3_2_1_10_1","unstructured":"Tri Dao Daniel Y. Fu Stefano Ermon Atri Rudra and Christopher R\u00e9. 2022. FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_11_1","volume-title":"Classeval: A manually-crafted benchmark for evaluating llms on class-level code generation. arXiv preprint arXiv:2308.01861.","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."},{"key":"e_1_3_2_1_12_1","unstructured":"Google Cloud. 2020. bfloat16: The secret to high performance on Cloud TPUs. https:\/\/cloud.google.com\/blog\/products\/ai-machine-learning\/bfloat16-the-secret-to-high-performance-on-cloud-tpus Accessed: 2023-04-04"},{"key":"e_1_3_2_1_13_1","volume-title":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2534\u20132544","author":"Gu Wenchao","year":"2022","unstructured":"Wenchao Gu, Yanlin Wang, Lun Du, Hongyu Zhang, Shi Han, Dongmei Zhang, and Michael Lyu. 2022. Accelerating Code Search with Deep Hashing and Code Classification. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2534\u20132544."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.499"},{"key":"e_1_3_2_1_15_1","volume-title":"Toolkengpt: Augmenting frozen language models with massive tools via tool embeddings. Advances in neural information processing systems, 36","author":"Hao Shibo","year":"2024","unstructured":"Shibo Hao, Tianyang Liu, Zhen Wang, and Zhiting Hu. 2024. Toolkengpt: Augmenting frozen language models with massive tools via tool embeddings. Advances in neural information processing systems, 36 (2024)."},{"key":"e_1_3_2_1_16_1","unstructured":"Shirley Anugrah Hayati Raphael Olivier Pravalika Avvaru Pengcheng Yin Anthony Tomasic and Graham Neubig. 2018. Retrieval-based neural code generation. In EMNLP."},{"key":"e_1_3_2_1_17_1","volume-title":"Answering the call for a standard reliability measure for coding data. Communication methods and measures, 1, 1","author":"Hayes Andrew F","year":"2007","unstructured":"Andrew F Hayes and Klaus Krippendorff. 2007. Answering the call for a standard reliability measure for coding data. Communication methods and measures, 1, 1 (2007), 77\u201389."},{"key":"e_1_3_2_1_18_1","unstructured":"Baizhou Huang Shuai Lu Weizhu Chen Xiaojun Wan and Nan Duan. 2023. Enhancing Large Language Models in Coding Through Multi-Perspective Self-Consistency. arXiv preprint arXiv:2309.17272."},{"key":"e_1_3_2_1_19_1","unstructured":"Naman Jain Tianjun Zhang Wei-Lin Chiang Joseph E Gonzalez Koushik Sen and Ion Stoica. 2023. LLM-Assisted Code Cleaning For Training Accurate Code Generators. arXiv preprint arXiv:2311.14904."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Hui Jiang Chulun Zhou Fandong Meng Biao Zhang Jie Zhou Degen Huang Qingqiang Wu and Jinsong Su. 2021. Exploring dynamic selection of branch expansion orders for code generation. arXiv preprint arXiv:2106.00261.","DOI":"10.18653\/v1\/2021.acl-long.394"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3600006.3613165"},{"key":"e_1_3_2_1_22_1","first-page":"21314","article-title":"Coderl: Mastering code generation through pretrained models and deep reinforcement learning","volume":"35","author":"Le Hung","year":"2022","unstructured":"Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, and Steven Chu Hong Hoi. 2022. Coderl: Mastering code generation through pretrained models and deep reinforcement learning. Advances in Neural Information Processing Systems, 35 (2022), 21314\u201321328.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3383458"},{"key":"e_1_3_2_1_24_1","volume-title":"International Conference on Machine Learning. 19274\u201319286","author":"Leviathan Yaniv","year":"2023","unstructured":"Yaniv Leviathan, Matan Kalman, and Yossi Matias. 2023. Fast inference from transformers via speculative decoding. In International Conference on Machine Learning. 19274\u201319286."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Jia Li Ge Li Yongmin Li and Zhi Jin. 2023. Structured Chain-of-Thought Prompting for Code Generation. arXiv preprint arXiv:2305.06599.","DOI":"10.1145\/3690635"},{"key":"e_1_3_2_1_26_1","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."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Jia Li Yunfei Zhao Yongmin Li Ge Li and Zhi Jin. 2024. AceCoder: An Effective Prompting Technique Specialized in Code Generation. ACM Transactions on Software Engineering and Methodology.","DOI":"10.1145\/3675395"},{"key":"e_1_3_2_1_28_1","volume-title":"Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, and Jenny Chim.","author":"Li Raymond","year":"2023","unstructured":"Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, and Jenny Chim. 2023. StarCoder: may the source be with you!. arXiv preprint arXiv:2305.06161."},{"key":"e_1_3_2_1_29_1","unstructured":"Xin-Ye Li Jiang-Tian Xue Zheng Xie and Ming Li. 2023. Think Outside the Code: Brainstorming Boosts Large Language Models in Code Generation. arXiv preprint arXiv:2305.10679."},{"key":"e_1_3_2_1_30_1","volume-title":"Tom\u00e1\u0161 Ko\u010disk\u1ef3, Andrew Senior, Fumin Wang, and Phil Blunsom.","author":"Ling Wang","year":"2016","unstructured":"Wang Ling, Edward Grefenstette, Karl Moritz Hermann, Tom\u00e1\u0161 Ko\u010disk\u1ef3, Andrew Senior, Fumin Wang, and Phil Blunsom. 2016. Latent predictor networks for code generation. In ACL."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Yue Liu Thanh Le-Cong Ratnadira Widyasari Chakkrit Tantithamthavorn Li Li Xuan-Bach D Le and David Lo. 2023. Refining ChatGPT-generated code: Characterizing and mitigating code quality issues. arXiv preprint arXiv:2307.12596.","DOI":"10.1145\/3643674"},{"key":"e_1_3_2_1_32_1","unstructured":"Ilya Loshchilov and Frank Hutter. 2017. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101."},{"key":"e_1_3_2_1_33_1","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."},{"key":"e_1_3_2_1_34_1","volume-title":"Swayam Singh, Xiangru Tang, Leandro von Werra, and Shayne Longpre.","author":"Muennighoff Niklas","year":"2023","unstructured":"Niklas Muennighoff, Qian Liu, Armel Zebaze, Qinkai Zheng, Binyuan Hui, Terry Yue Zhuo, Swayam Singh, Xiangru Tang, Leandro von Werra, and Shayne Longpre. 2023. Octopack: Instruction tuning code large language models. arXiv preprint arXiv:2308.07124."},{"key":"e_1_3_2_1_35_1","volume-title":"International Conference on Machine Learning. 26106\u201326128","author":"Ni Ansong","year":"2023","unstructured":"Ansong Ni, Srini Iyer, Dragomir Radev, Veselin Stoyanov, Wen-tau Yih, Sida Wang, and Xi Victoria Lin. 2023. Lever: Learning to verify language-to-code generation with execution. In International Conference on Machine Learning. 26106\u201326128."},{"key":"e_1_3_2_1_36_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence. 37","author":"Nie Lunyiu","year":"2023","unstructured":"Lunyiu Nie, Jiuding Sun, Yanlin Wang, Lun Du, Shi Han, Dongmei Zhang, Lei Hou, Juanzi Li, and Jidong Zhai. 2023. Unveiling the black box of PLMs with semantic anchors: towards interpretable neural semantic parsing. In Proceedings of the AAAI Conference on Artificial Intelligence. 37, 13400\u201313408."},{"key":"e_1_3_2_1_37_1","volume-title":"Chenglong Wang, Jianfeng Gao, and Armando Solar-Lezama.","author":"Olausson Theo X","year":"2023","unstructured":"Theo X Olausson, Jeevana Priya Inala, Chenglong Wang, Jianfeng Gao, and Armando Solar-Lezama. 2023. Demystifying GPT Self-Repair for Code Generation. arXiv preprint arXiv:2306.09896."},{"key":"e_1_3_2_1_38_1","unstructured":"OpenAI. 2021. OpenAI Code. https:\/\/openai.com\/blog\/openai-code"},{"key":"e_1_3_2_1_39_1","unstructured":"OpenAI. 2022. ChatGPT. https:\/\/openai.com\/blog\/chatgpt"},{"key":"e_1_3_2_1_40_1","unstructured":"OpenAI. 2023. gpt-3.5-turbo. https:\/\/platform.openai.com\/docs\/models\/gpt-3-5-turbo"},{"key":"e_1_3_2_1_41_1","unstructured":"OpenAI. 2023. GPT-4. https:\/\/openai.com\/research\/gpt-4"},{"key":"e_1_3_2_1_42_1","unstructured":"Phind. 2023. Phind-CodeLlama-34B-v2. https:\/\/huggingface.co\/Phind\/Phind-CodeLlama-34B-v2 Accessed: 2023-11-21"},{"key":"e_1_3_2_1_43_1","unstructured":"Alec Radford Karthik Narasimhan Tim Salimans and Ilya Sutskever. 2018. Improving language understanding by generative pre-training."},{"key":"e_1_3_2_1_44_1","volume-title":"Yossi Adi, Jingyu Liu, Tal Remez, and J\u00e9r\u00e9my Rapin.","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, and J\u00e9r\u00e9my Rapin. 2023. Code llama: Open foundation models for code. arXiv preprint arXiv:2308.12950."},{"key":"e_1_3_2_1_45_1","unstructured":"Ensheng Shi Yanlin Wang Hongyu Zhang Lun Du Shi Han Dongmei Zhang and Hongbin Sun. 2023. Towards Efficient Fine-tuning of Pre-trained Code Models: An Experimental Study and Beyond. arXiv preprint arXiv:2304.05216."},{"key":"e_1_3_2_1_46_1","unstructured":"Ensheng Shi Fengji Zhang Yanlin Wang Bei Chen Lun Du Hongyu Zhang Shi Han Dongmei Zhang and Hongbin Sun. 2023. SoTaNa: The Open-Source Software Development Assistant. arXiv preprint arXiv:2308.13416."},{"key":"e_1_3_2_1_47_1","volume-title":"2024 IEEE\/ACM 46th International Conference on Software Engineering (ICSE). 906\u2013917","author":"Sun Zhensu","year":"2024","unstructured":"Zhensu Sun, Xiaoning Du, Fu Song, Shangwen Wang, and Li Li. 2024. When Neural Code Completion Models Size up the Situation: Attaining Cheaper and Faster Completion through Dynamic Model Inference. In 2024 IEEE\/ACM 46th International Conference on Software Engineering (ICSE). 906\u2013917."},{"key":"e_1_3_2_1_48_1","volume-title":"2023 IEEE\/ACM 45th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). 324\u2013325","author":"Sun Zhensu","year":"2023","unstructured":"Zhensu Sun, Xiaoning Du, Fu Song, Shangwen Wang, Mingze Ni, and Li Li. 2023. Don\u2019t Complete It! Preventing Unhelpful Code Completion for Productive and Sustainable Neural Code Completion Systems. In 2023 IEEE\/ACM 45th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion). 324\u2013325."},{"key":"e_1_3_2_1_49_1","volume-title":"AI Coders Are Among Us: Rethinking Programming Language Grammar Towards Efficient Code Generation. In 33st ACM SIGSOFT International Symposium on Software Testing and Analysis, ISSTA","author":"Sun Zhensu","year":"2024","unstructured":"Zhensu Sun, Xiaoning Du, Zhou Yang, Li Li, and David Lo. 2024. AI Coders Are Among Us: Rethinking Programming Language Grammar Towards Efficient Code Generation. In 33st ACM SIGSOFT International Symposium on Software Testing and Analysis, ISSTA 2024."},{"key":"e_1_3_2_1_50_1","unstructured":"Hugo Touvron Louis Martin Kevin Stone Peter Albert Amjad Almahairi Yasmine Babaei Nikolay Bashlykov Soumya Batra Prajjwal Bhargava and Shruti Bhosale. 2023. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288."},{"key":"e_1_3_2_1_51_1","volume-title":"\u0141 ukasz Kaiser, and Illia Polosukhin","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141 ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems, 30 (2017)."},{"key":"e_1_3_2_1_52_1","volume-title":"Proceedings of the AAAI conference on artificial intelligence. 35","author":"Wang Yanlin","year":"2021","unstructured":"Yanlin Wang and Hui Li. 2021. Code completion by modeling flattened abstract syntax trees as graphs. In Proceedings of the AAAI conference on artificial intelligence. 35, 14015\u201314023."},{"key":"e_1_3_2_1_53_1","unstructured":"Yanlin Wang Yanli Wang Daya Guo Jiachi Chen Ruikai Zhang Yuchi Ma and Zibin Zheng. 2024. RLCoder: Reinforcement Learning for Repository-Level Code Completion."},{"key":"e_1_3_2_1_54_1","volume-title":"International Conference on Machine Learning.","author":"Wei Yuxiang","year":"2023","unstructured":"Yuxiang Wei, Zhe Wang, Jiawei Liu, Yifeng Ding, and Lingming Zhang. 2023. Magicoder: Source code is all you need. In International Conference on Machine Learning."},{"key":"e_1_3_2_1_55_1","volume-title":"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 14520\u201314535","author":"Xiao Ruixuan","year":"2023","unstructured":"Ruixuan Xiao, Yiwen Dong, Junbo Zhao, Runze Wu, Minmin Lin, Gang Chen, and Haobo Wang. 2023. FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 14520\u201314535."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"crossref","unstructured":"Prateek Yadav Qing Sun Hantian Ding Xiaopeng Li Dejiao Zhang Ming Tan Xiaofei Ma Parminder Bhatia Ramesh Nallapati and Murali Krishna Ramanathan. 2023. Exploring continual learning for code generation models. arXiv preprint arXiv:2307.02435.","DOI":"10.18653\/v1\/2023.acl-short.68"},{"key":"e_1_3_2_1_57_1","volume-title":"CoderEval: A Benchmark of Pragmatic Code Generation with Generative Pretrained Models. In 2024 IEEE\/ACM 46th International Conference on Software Engineering (ICSE). 428\u2013439","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 Pretrained Models. In 2024 IEEE\/ACM 46th International Conference on Software Engineering (ICSE). 428\u2013439."},{"key":"e_1_3_2_1_58_1","unstructured":"Daoguang Zan Bei Chen Yongshun Gong Junzhi Cao Fengji Zhang Bingchao Wu Bei Guan Yilong Yin and Yongji Wang. 2023. Private-library-oriented code generation with large language models. arXiv preprint arXiv:2307.15370."},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.acl-long.411"},{"key":"e_1_3_2_1_60_1","volume-title":"Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2471\u20132484","author":"Zhang Fengji","year":"2023","unstructured":"Fengji Zhang, Bei Chen, Yue Zhang, Jacky Keung, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, and Weizhu Chen. 2023. RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2471\u20132484."},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"crossref","unstructured":"Jun Zhang Jue Wang Huan Li Lidan Shou Ke Chen Gang Chen and Sharad Mehrotra. 2023. Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding. arXiv preprint arXiv:2309.08168.","DOI":"10.18653\/v1\/2024.acl-long.607"},{"key":"e_1_3_2_1_62_1","volume-title":"Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 769\u2013787","author":"Zhang Kechi","year":"2023","unstructured":"Kechi Zhang, Zhuo Li, Jia Li, Ge Li, and Zhi Jin. 2023. Self-Edit: Fault-Aware Code Editor for Code Generation. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 769\u2013787."},{"key":"e_1_3_2_1_63_1","unstructured":"Shun Zhang Zhenfang Chen Yikang Shen Mingyu Ding Joshua B Tenenbaum and Chuang Gan. 2023. Planning with large language models for code generation. arXiv preprint arXiv:2303.05510."},{"key":"e_1_3_2_1_64_1","unstructured":"Ziyin Zhang Chaoyu Chen Bingchang Liu Cong Liao Zi Gong Hang Yu Jianguo Li and Rui Wang. 2023. A Survey on Language Models for Code. arXiv preprint arXiv:2311.07989."},{"key":"e_1_3_2_1_65_1","unstructured":"Wayne Xin Zhao Kun Zhou Junyi Li Tianyi Tang Xiaolei Wang Yupeng Hou Yingqian Min Beichen Zhang Junjie Zhang and Zican Dong. 2023. A survey of large language models. arXiv preprint arXiv:2303.18223."},{"key":"e_1_3_2_1_66_1","unstructured":"Dewu Zheng Yanlin Wang Ensheng Shi Ruikai Zhang Yuchi Ma Hongyu Zhang and Zibin Zheng. 2024. Towards more realistic evaluation of LLM-based code generation: an experimental study and beyond. arXiv preprint arXiv:2406.06918."},{"key":"e_1_3_2_1_67_1","unstructured":"Zibin Zheng Kaiwen Ning Yanlin Wang Jingwen Zhang Dewu Zheng Mingxi Ye and Jiachi Chen. 2023. A Survey of Large Language Models for Code: Evolution Benchmarking and Future Trends. arXiv preprint arXiv:2311.10372."},{"key":"e_1_3_2_1_68_1","volume-title":"Ge Li, YunFei Zhao, Jia Li, Zhi Jin, and Hong Mei.","author":"Zhu Yuqi","year":"2023","unstructured":"Yuqi Zhu, Jia Allen Li, Ge Li, YunFei Zhao, Jia Li, Zhi Jin, and Hong Mei. 2023. Improving Code Generation by Dynamic Temperature Sampling. arXiv preprint arXiv:2309.02772."}],"event":{"name":"ISSTA '24: 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis","location":"Vienna Austria","acronym":"ISSTA '24","sponsor":["SIGSOFT ACM Special Interest Group on Software Engineering","AITO"]},"container-title":["Proceedings of the 33rd ACM SIGSOFT International Symposium on Software Testing and Analysis"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3650212.3680343","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3650212.3680343","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T22:50:07Z","timestamp":1750287007000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3650212.3680343"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,11]]},"references-count":68,"alternative-id":["10.1145\/3650212.3680343","10.1145\/3650212"],"URL":"https:\/\/doi.org\/10.1145\/3650212.3680343","relation":{},"subject":[],"published":{"date-parts":[[2024,9,11]]},"assertion":[{"value":"2024-09-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}