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Recently, the large language models (LLMs) have shown extraordinary ability in understanding natural language and generating programming code.
Abstract syntax trees-and their role in model driven software development
Fischer, G.; Lusiardi, J.; and Von Gudenberg, J. W. 2007 · 2007
Earlier work this paper cites.
Mining succinct and high-coverage API usage patterns from source code
Wang, J.; Dang, Y.; Zhang, H.; Chen, K.; Xie, T.; and Zhang, D. 2013 · 2013
Earlier work this paper cites.
Mining preconditions of APIs in large-scale code corpus
Nguyen, H. A.; Dyer, R.; Nguyen, T. N.; and Rajan, H. 2014 · 2014
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From query to usable code: an analysis of stack overflow code snippets
Yang, D.; Hussain, A.; and Lopes, C. V. 2016 · 2016
Earlier work this paper cites.
API deprecation: a retrospective analysis and detection method for code examples on the web
Zhou, J.; and Walker, R. J. 2016 · 2016
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Stack overflow considered harmful? the impact of copy&paste on android application security
Fischer, F.; Böttinger, K.; Xiao, H.; Stransky, C.; Acar, Y.; Backes, M.; and Fahl, S. 2017 · 2017
Earlier work this paper cites.
DeepSeek: Content based image search & retrieval
Piplani, T.; and Bamman, D. 2018 · 2018
Earlier work this paper cites.
Learning to mine aligned code and natural language pairs from stack overflow
Yin, P.; Deng, B.; Chen, E.; Vasilescu, B.; and Neubig, G. 2018 · 2018
Earlier work this paper cites.
Are code examples on an online Q&A forum reliable?: a study of API misuse on stack overflow. In 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE)
Zhang, T.; Upadhyaya, G.; Reinhardt, A.; Rajan, H.; and Kim, M. 2018 · 2018
Earlier work this paper cites.
Program synthesis with large language models
Austin, J.; Odena, A.; Nye, M.; Bosma, M.; Michalewski, H.; Dohan, D.; Jiang, E.; Cai, C.; Terry, M.; Le, Q.; et al. 2021 · 2021
Earlier work this paper cites.
Evaluating large language models trained on code
Chen, M.; Tworek, J.; Jun, H.; Yuan, Q.; Pinto, H. P. d. O.; Kaplan, J.; Edwards, H.; Burda, Y.; Joseph, N.; Brockman, G.; et al. 2021 · 2021
Cited alongside, same era.
Measuring coding challenge competence with apps
Hendrycks, D.; Basart, S.; Kadavath, S.; Mazeika, M.; Arora, A.; Guo, E.; Burns, C.; Puranik, S.; He, H.; Song, D.; et al. 2021 · 2021
Cited alongside, same era.
Codexglue: A machine learning benchmark dataset for code understanding and generation
Lu, S.; Guo, D.; Ren, S.; Huang, J.; Svyatkovskiy, A.; Blanco, A.; Clement, C.; Drain, D.; Jiang, D.; Tang, D.; et al. 2021 · 2021
Cited alongside, same era.
Asleep at the keyboard? assessing the security of github copilot’s code contributions
Pearce, H.; Ahmad, B.; Tan, B.; Dolan-Gavitt, B.; and Karri, R. 2022 · 2022
Cited alongside, same era.
Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality
Chiang, W.-L.; Li, Z.; Lin, Z.; Sheng, Y.; Wu, Z.; Zhang, H.; Zheng, L.; Zhuang, S.; Zhuang, Y.; Gonzalez, J. E.; Stoica, I.; and Xing, E. P. 2023 · 2023
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Protecting data integrity of web applications with database constraints inferred from application code
Huang, H.; Shen, B.; Zhong, L.; and Zhou, Y. 2023 · 2023
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Large Language Models and Simple, Stupid Bugs
Jesse, K.; Ahmed, T.; Devanbu, P. T.; and Morgan, E. 2023 · 2023
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SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
Jimenez, C. E.; Yang, J.; Wettig, A.; Yao, S.; Pei, K.; Press, O.; and Narasimhan, K. 2023 · 2023
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Perry, N.; Srivastava, M.; Kumar, D.; and Boneh, D. 2022 · 2022
Cited alongside, same era.
Synchromesh: Reliable code generation from pre-trained language models
Poesia, G.; Polozov, O.; Le, V.; Tiwari, A.; Soares, G.; Meek, C.; and Gulwani, S. 2022 · 2022
Cited alongside, same era.
An Empirical Study of Code Smells in Transformer-based Code Generation Techniques
Siddiq, M. L.; Majumder, S. H.; Mim, M. R.; Jajodia, S.; and Santos, J. C. 2022 · 2022
Cited alongside, same era.
A systematic evaluation of large language models of code
Xu, F. F.; Alon, U.; Neubig, G.; and Hellendoorn, V. J. 2022 · 2022
Cited alongside, same era.
Assessing the quality of GitHub copilot’s code generation
Yetistiren, B.; Ozsoy, I.; and Tuzun, E. 2022 · 2022
Cited alongside, same era.
Anil, R.; Dai, A. M.; Firat, O.; Johnson, M.; Lepikhin, D.; Passos, A.; Shakeri, S.; Taropa, E.; Bailey, P.; Chen, Z.; et al. 2023 · 2023
Cited alongside, same era.
OpenAI. 2023a
Cited in the paper.
OpenAI. 2023b
Cited in the paper.
Liu, J.; Xia, C. S.; Wang, Y.; and Zhang, L. 2023 · 2023
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WizardCoder: Empowering Code Large Language Models with Evol-Instruct
Luo, Z.; Xu, C.; Zhao, P.; Sun, Q.; Geng, X.; Hu, W.; Tao, C.; Ma, J.; Lin, Q.; and Jiang, D. 2023 · 2023
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Gorilla: Large language model connected with massive apis
Patil, S. G.; Zhang, T.; Wang, X.; and Gonzalez, J. E. 2023 · 2023
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Lost at c: A user study on the security implications of large language model code assistants
Sandoval, G.; Pearce, H.; Nys, T.; Karri, R.; Garg, S.; and Dolan-Gavitt, B. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Touvron, H.; Martin, L.; Stone, K.; Albert, P.; Almahairi, A.; Babaei, Y.; Bashlykov, N.; Batra, S.; Bhargava, P.; Bhosale, S.; et al. 2023 · 2023
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A comprehensive capability analysis of gpt-3 and gpt-3.5 series models
Ye, J.; Chen, X.; Xu, N.; Zu, C.; Shao, Z.; Liu, S.; Cui, Y.; Zhou, Z.; Gong, C.; Shen, Y.; et al. 2023 · 2023
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