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Technical Q&A sites are valuable for software developers seeking knowledge, but the code snippets they provide are often uncompilable and incomplete due to unresolved types and missing libraries.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
Earlier work this paper cites.
SnR: constraint-based type inference for incomplete Java code snippets. In Proceedings of the 44th International Conference on Software Engineering . 1982–1993
Yiwen Dong, Tianxiao Gu, Yongqiang Tian, and Chengnian Sun. 2022 · 1993
Earlier work this paper cites.
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen tau Yih, Tim Rocktäschel, Sebastian Riedel, and Douwe Kiela. 2021 · 2005
Earlier work this paper cites.
Typestate-based semantic code search over partial programs. In Proceedings of the ACM international conference on Object oriented programming systems languages and applications . 997–1016
Alon Mishne, Sharon Shoham, and Eran Yahav. 2012 · 2012
Earlier work this paper cites.
Live API documentation. In Proceedings of the 36th international conference on software engineering . 643–652
Siddharth Subramanian, Laura Inozemtseva, and Reid Holmes. 2014 · 2014
Earlier work this paper cites.
Fixing recurring crash bugs via analyzing q&a sites (T). In 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 307–318
Qing Gao, Hansheng Zhang, Jie Wang, Yingfei Xiong, Lu Zhang, and Hong Mei. 2015 · 2015
Earlier work this paper cites.
How developers search for code: a case study. In Proceedings of the 2015 10th joint meeting on foundations of software engineering . 191–201
Caitlin Sadowski, Kathryn T Stolee, and Sebastian Elbaum. 2015 · 2015
Earlier work this paper cites.
CSNIPPEX: automated synthesis of compilable code snippets from Q&A sites. In Proceedings of the 25th international symposium on software testing and analysis . 118–129
Valerio Terragni, Yepang Liu, and Shing-Chi Cheung. 2016 · 2016
Earlier work this paper cites.
What do developers search for on the web?
Xin Xia, Lingfeng Bao, David Lo, Pavneet Singh Kochhar, Ahmed E Hassan, and Zhenchang Xing. 2017 · 2017
Earlier work this paper cites.
Statistical learning of api fully qualified names in code snippets of online forums. In Proceedings of the 40th International Conference on Software Engineering . 632–642
Hung Phan, Hoan Anh Nguyen, Ngoc M Tran, Linh H Truong, Anh Tuan Nguyen, and Tien N Nguyen. 2018 · 2018
Earlier work this paper cites.
Learning from examples to find fully qualified names of api elements in code snippets. In 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 243–254
CM Khaled Saifullah, Muhammad Asaduzzaman, and Chanchal K Roy. 2019 · 2019
Earlier work this paper cites.
How do developers utilize source code from stack overflow?
Yuhao Wu, Shaowei Wang, Cor-Paul Bezemer, and Katsuro Inoue. 2019 · 2019
Earlier work this paper cites.
Analyzing and supporting adaptation of online code examples. In 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) . IEEE, 316–327
Tianyi Zhang, Di Yang, Crista Lopes, and Miryung Kim. 2019 · 2019
Earlier work this paper cites.
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, et al · 2020
Earlier work this paper cites.
PostFinder: Mining Stack Overflow posts to support software developers
Riccardo Rubei, Claudio Di Sipio, Phuong T Nguyen, Juri Di Rocco, and Davide Di Ruscio. 2020 · 2020
Earlier work this paper cites.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
Earlier work this paper cites.
Making Pre-trained Language Models Better Few-shot Learners. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) . Association for Computational Linguistics, 3816–3830
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
Earlier work this paper cites.
Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2021 · 2021
Cited alongside, same era.
APIzation: Generating reusable APIs from StackOverflow code snippets. In 2021 36th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 542–554
Valerio Terragni and Pasquale Salza. 2021 · 2021
Cited alongside, same era.
The prevalence of code smells in machine learning projects. In 2021 IEEE/ACM 1st Workshop on AI Engineering-Software Engineering for AI (WAIN) . IEEE, 1–8
Bart Van Oort, Luís Cruz, Maurício Aniche, and Arie Van Deursen. 2021 · 2021
Cited alongside, same era.
Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi. 2021 · 2021
Cited alongside, same era.
Retrieval-based prompt selection for code-related few-shot learning. In Proceedings of the 45th International Conference on Software Engineering (ICSE’23)
Noor Nashid, Mifta Sintaha, and Ali Mesbah. 2023 · 2023
Later among the works it cites.
AI-assisted coding: Experiments with GPT-4
Russell A Poldrack, Thomas Lu, and Gašper Beguš. 2023 · 2023
Later among the works it cites.
Adaptive test generation using a large language model
Max Schäfer, Sarah Nadi, Aryaz Eghbali, and Frank Tip. 2023 · 2023
Later among the works it cites.
Use chat gpt to solve programming bugs
Nigar M Shafiq Surameery and Mohammed Y Shakor. 2023 · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
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Albert Webson and Ellie Pavlick. 2021 · 2021
Cited alongside, same era.
A study of c/c++ code weaknesses on stack overflow
Haoxiang Zhang, Shaowei Wang, Heng Li, Tse-Hsun Chen, and Ahmed E Hassan. 2021 · 2021
Cited alongside, same era.
Prompt-tuned code language model as a neural knowledge base for type inference in statically-typed partial code. In Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering . 1–13
Qing Huang, Zhiqiang Yuan, Zhenchang Xing, Xiwei Xu, Liming Zhu, and Qinghua Lu. 2022 · 2022
Cited alongside, same era.
Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
Cited alongside, same era.
Codegen: An open large language model for code with multi-turn program synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong. 2022 · 2022
Cited alongside, same era.
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Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, and Denny Zhou. 2022 · 2022
Cited alongside, same era.
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Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2022b · 2022
Cited alongside, same era.
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2023 · 2023
Cited alongside, same era.
Yuxiang Wei, Chunqiu Steven Xia, and Lingming Zhang. 2023 · 2023
Later among the works it cites.
A prompt pattern catalog to enhance prompt engineering with chatgpt
Jules White, Quchen Fu, Sam Hays, Michael Sandborn, Carlos Olea, Henry Gilbert, Ashraf Elnashar, Jesse Spencer-Smith, and Douglas C Schmidt. 2023 · 2023
Later among the works it cites.
AutoGen: Enabling next-gen LLM applications via multi-agent conversation framework
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. 2023 · 2023
Later among the works it cites.
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Zhiheng Xi, Wenxiang Chen, Xin Guo, Wei He, Yiwen Ding, Boyang Hong, Ming Zhang, Junzhe Wang, Senjie Jin, Enyu Zhou, Rui Zheng, Xiaoran Fan, Xiao Wang, Limao Xiong, Yuhao Zhou, Weiran Wang, Changhao Jiang, Yicheng Zou, Xiangyang Liu, Zhangyue Yin, Shihan Dou, Rongxiang Weng, Wensen Cheng, Qi Zhang, Wenjuan Qin, Yongyan Zheng, Xipeng Qiu, Xuanjing Huang, and Tao Gui. 2023 · 2023
Later among the works it cites.
Conversational automated program repair
Chunqiu Steven Xia and Lingming Zhang. 2023 · 2023
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ChatUniTest: a ChatGPT-based automated unit test generation tool
Zhuokui Xie, Yinghao Chen, Chen Zhi, Shuiguang Deng, and Jianwei Yin. 2023 · 2023
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STEAM: Simulating the InTeractive BEhavior of ProgrAMmers for Automatic Bug Fixing
Yuwei Zhang, Zhi Jin, Ying Xing, and Ge Li. 2023 · 2023
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Lost in translation: A study of bugs introduced by large language models while translating code. In Proceedings of the IEEE/ACM 46th International Conference on Software Engineering . 1–13
Rangeet Pan, Ali Reza Ibrahimzada, Rahul Krishna, Divya Sankar, Lambert Pouguem Wassi, Michele Merler, Boris Sobolev, Raju Pavuluri, Saurabh Sinha, and Reyhaneh Jabbarvand. 2024 · 2024
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