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Large Language Models (LLMs) have made tremendous strides in code generation, but existing research fails to account for the dynamic nature of software development, marked by frequent library updates.
How do apis evolve? A story of refactoring
Danny Dig and Ralph E. Johnson · 2006
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Defects4j: a database of existing faults to enable controlled testing studies for java programs
René Just, Darioush Jalali, and Michael D. Ernst · 2014
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On the use of replacement messages in API deprecation: An empirical study
Gleison Brito, André C. Hora, Marco Túlio Valente, and Romain Robbes · 2017
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Quixbugs: a multi-lingual program repair benchmark set based on the quixey challenge
Derrick Lin, James Koppel, Angela Chen, and Armando Solar-Lezama · 2017
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Staqc: A systematically mined question-code dataset from stack overflow
Ziyu Yao, Daniel S. Weld, Wei-Peng Chen, and Huan Sun · 2018
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Learning to mine aligned code and natural language pairs from stack overflow
Pengcheng Yin, Bowen Deng, Edgar Chen, Bogdan Vasilescu, and Graham Neubig · 2018
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Codebert: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou · 2020
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Exploring how deprecated python library apis are (not) handled
Jiawei Wang, Li Li, Kui Liu, and Haipeng Cai · 2020
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How do python framework apis evolve? an exploratory study
Zhaoxu Zhang, Hengcheng Zhu, Ming Wen, Yida Tao, Yepang Liu, and Yingfei Xiong · 2020
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Program synthesis with large language models
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
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Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Pondé de Oliveira Pinto, Jared Kaplan, Harrison Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
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Understanding software-2.0: A study of machine learning library usage and evolution
Malinda Dilhara, Ameya Ketkar, and Danny Dig · 2021
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Characterization and automatic updates of deprecated machine-learning API usages
Stefanus A. Haryono, Ferdian Thung, David Lo, Julia Lawall, and Lingxiao Jiang · 2021
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Measuring coding challenge competence with APPS
Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, and Jacob Steinhardt · 2021
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Identifying and characterizing silently-evolved methods in the android API
Pei Liu, Li Li, Yichun Yan, Mattia Fazzini, and John C. Grundy · 2021
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Codexglue: A machine learning benchmark dataset for code understanding and generation
Shuai Lu, Daya Guo, Shuo Ren, Junjie Huang, Alexey Svyatkovskiy, Ambrosio Blanco, Colin B. Clement, Dawn Drain, Daxin Jiang, Duyu Tang, et al · 2021
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Apiscanner - towards automated detection of deprecated apis in python libraries
Aparna Vadlamani, Rishitha Kalicheti, and Sridhar Chimalakonda · 2021
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Unveiling the mystery of API evolution in deep learning frameworks: A case study of tensorflow 2
Zejun Zhang, Yanming Yang, Xin Xia, David Lo, Xiaoxue Ren, and John C. Grundy · 2021
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Temporalwiki: A lifelong benchmark for training and evaluating ever-evolving language models
Joel Jang, Seonghyeon Ye, Changho Lee, Sohee Yang, Joongbo Shin, Janghoon Han, Gyeonghun Kim, and Minjoon Seo · 2022
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A systematic review of API evolution literature
Maxime Lamothe, Yann-Gaël Guéhéneuc, and Weiyi Shang · 2022
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Competition-level code generation with alphacode
Yujia Li, David H. Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, et al · 2022
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Pretrained language model in continual learning: A comparative study
Tongtong Wu, Massimo Caccia, Zhuang Li, Yuan-Fang Li, Guilin Qi, and Gholamreza Haffari · 2022
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Guiding language models of code with global context using monitors
Lakshya A Agrawal, Aditya Kanade, Navin Goyal, Shuvendu K. Lahiri, and Sriram K. Rajamani · 2023
Cited alongside, same era.
AVATAR: A parallel corpus for java-python program translation
Wasi Uddin Ahmad, Md Golam Rahman Tushar, Saikat Chakraborty, and Kai-Wei Chang · 2023
Cited alongside, same era.
Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al · 2023
Quanjun Zhang, Tongke Zhang, Juan Zhai, Chunrong Fang, Bowen Yu, Weisong Sun, and Zhenyu Chen · 2023
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Codegeex: A pre-trained model for code generation with multilingual evaluations on humaneval-x
Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Zihan Wang, Lei Shen, Andi Wang, Yang Li, et al · 2023
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aixcoder-7b code large language model
aiXcoder team · 2024
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Codegemma: Open code models based on gemma
CodeGemma Team, Ale Jakse Hartman, Andrea Hu, Christopher A. Choquette-Choo, Heri Zhao, Jane Fine, Jeffrey Hui, et al · 2024
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Codegrag: Extracting composed syntax graphs for retrieval augmented cross-lingual code generation
Kounianhua Du, Renting Rui, Huacan Chai, Lingyue Fu, Wei Xia, Yasheng Wang, Ruiming Tang, Yong Yu, and Weinan Zhang · 2024
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Cited alongside, same era.
Incoder: A generative model for code infilling and synthesis
Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Scott Yih, Luke Zettlemoyer, and Mike Lewis · 2023
Cited alongside, same era.
Codeapex: A bilingual programming evaluation benchmark for large language models
Lingyue Fu, Huacan Chai, Shuang Luo, Kounianhua Du, Weiming Zhang, Longteng Fan, Jiayi Lei, Renting Rui, Jianghao Lin, Yuchen Fang, et al · 2023
Cited alongside, same era.
Retrieval-augmented generation for large language models: A survey
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Qianyu Guo, Meng Wang, and Haofen Wang · 2023
Cited alongside, same era.
Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, et al · 2023
Cited alongside, same era.
Instructcoder: Empowering language models for code editing
Qisheng Hu, Kaixin Li, Xu Zhao, Yuxi Xie, Tiedong Liu, Hui Chen, Qizhe Xie, and Junxian He · 2023
Cited alongside, same era.
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de Las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al · 2023
Cited alongside, same era.
On the evaluation of neural code translation: Taxonomy and benchmark
Mingsheng Jiao, Tingrui Yu, Xuan Li, Guanjie Qiu, Xiaodong Gu, and Beijun Shen · 2023
Cited alongside, same era.
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Deepseek-coder: When the large language model meets programming - the rise of code intelligence
Daya Guo, Qihao Zhu, Dejian Yang, Zhenda Xie, Kai Dong, Wentao Zhang, Guanting Chen, Xiao Bi, Y. Wu, Y. K. Li, et al · 2024
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A survey on large language models for code generation
Juyong Jiang, Fan Wang, Jiasi Shen, Sungju Kim, and Sunghun Kim · 2024
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Starcoder 2 and the stack v2: The next generation
Anton Lozhkov, Raymond Li, Loubna Ben Allal, Federico Cassano, Joel Lamy-Poirier, Nouamane Tazi, Ao Tang, Dmytro Pykhtar, Jiawei Liu, Yuxiang Wei, Tianyang Liu, Max Tian, et al · 2024
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Llama 3 model card
Meta LlaMa team · 2024
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Gpt-3.5 turbo fine-tuning and api updates
OpenAI · 2024
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Hello gpt-4o
OpenAI · 2024
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Balanced data sampling for language model training with clustering
Yunfan Shao, Linyang Li, Zhaoye Fei, Hang Yan, Dahua Lin, and Xipeng Qiu · 2024
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Tokenization counts: the impact of tokenization on arithmetic in frontier llms
Aaditya K. Singh and DJ Strouse · 2024
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A survey of neural code intelligence: Paradigms, advances and beyond
Qiushi Sun, Zhirui Chen, Fangzhi Xu, Kanzhi Cheng, Chang Ma, Zhangyue Yin, Jianing Wang, Chengcheng Han, Renyu Zhu, Shuai Yuan, et al · 2024
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Debugbench: Evaluating debugging capability of large language models
Runchu Tian, Yining Ye, Yujia Qin, Xin Cong, Yankai Lin, Yinxu Pan, Yesai Wu, Haotian Hui, Weichuan Liu, Zhiyuan Liu, and Maosong Sun · 2024
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Continual learning for large language models: A survey
Tongtong Wu, Linhao Luo, Yuan-Fang Li, Shirui Pan, Thuy-Trang Vu, and Gholamreza Haffari · 2024
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Codereval: A benchmark of pragmatic code generation with generative pre-trained models
Hao Yu, Bo Shen, Dezhi Ran, Jiaxin Zhang, Qi Zhang, Yuchi Ma, Guangtai Liang, Ying Li, Qianxiang Wang, and Tao Xie · 2024
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Set the clock: Temporal alignment of pretrained language models
Bowen Zhao, Zander Brumbaugh, Yizhong Wang, Hannaneh Hajishirzi, and Noah A. Smith · 2024
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Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions
Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, Han Hu, Wenhao Yu, Ratnadira Widyasari, Imam Nur Bani Yusuf, Haolan Zhan, et al · 2024
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