Fetching the paper…
Reading the bibliography…
Control Flow Graphs (CFGs) are essential for visualizing, understanding and analyzing program behavior.
Control flow analysis
Frances E Allen · 1970
Earlier work this paper cites.
Language representation based on abstract syntax
Hans Diel · 1976
Earlier work this paper cites.
Soot: a java bytecode optimization framework
Raja Vallée-Rai, Phong Co, Etienne M. Gagnon, Laurie J. Hendren, Patrick Lam, and Vijay Sundaresan · 2010
Earlier work this paper cites.
On the naturalness of software
Premkumar T. Devanbu · 2012
Earlier work this paper cites.
Elements of survey sampling
Ravindra Singh and Naurang Singh Mangat · 2013
Earlier work this paper cites.
Dlint: Dynamically checking bad coding practices in javascript
Liang Gong, Michael Pradel, Manu Sridharan, and Koushik Sen · 2015
Earlier work this paper cites.
Spoon: A library for implementing analyses and transformations of java source code
Renaud Pawlak, Martin Monperrus, Nicolas Petitprez, Carlos Noguera, and Lionel Seinturier · 2016
Earlier work this paper cites.
Supervised deep features for software functional clone detection by exploiting lexical and syntactical information in source code
Huihui Wei and Ming Li · 2017
Earlier work this paper cites.
Deep code comment generation
Xing Hu, Ge Li, Xin Xia, David Lo, and Zhi Jin · 2018
Earlier work this paper cites.
WALA - static analysis framework for java
IBM · 2018
Earlier work this paper cites.
A survey of machine learning for big code and naturalness
Miltiadis Allamanis, Earl T. Barr, Premkumar T. Devanbu, and Charles Sutton · 2018
Earlier work this paper cites.
Your code is my code: Exploiting a common weakness in oauth 2.0 implementations
Wanpeng Li, Chris J Mitchell, and Thomas Chen · 2018
Earlier work this paper cites.
Capturing source code semantics via tree-based convolution over api-enhanced ast
Long Chen, Wei Ye, and Shikun Zhang · 2019
Earlier work this paper cites.
A novel neural source code representation based on abstract syntax tree
Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, Kaixuan Wang, and Xudong Liu · 2019
Earlier work this paper cites.
What do you learn from context? probing for sentence structure in contextualized word representations, 2019
Ian Tenney, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, R Thomas McCoy, Najoung Kim, Benjamin Van Durme, Samuel R. Bowman, Dipanjan Das, and Ellie Pavlick · 2019
Earlier work this paper cites.
Unified language model pre-training for natural language understanding and generation
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, M. Zhou, and Hsiao-Wuen Hon · 2019
Earlier work this paper cites.
A novel neural source code representation based on abstract syntax tree
Jian Zhang, Xu Wang, Hongyu Zhang, Hailong Sun, Kaixuan Wang, and Xudong Liu · 2019
Earlier work this paper cites.
Graphcodebert: Pre-training code representations with data flow
Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie Liu, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, et al · 2020
Earlier work this paper cites.
Detecting code clones with graph neural network and flow-augmented abstract syntax tree
Wenhan Wang, Ge Li, Bo Ma, Xin Xia, and Zhi Jin · 2020
Earlier work this paper cites.
Modular tree network for source code representation learning
Wenhan Wang, Ge Li, Sijie Shen, Xin Xia, and Zhi Jin · 2020
Earlier work this paper cites.
Automatically identifying words that can serve as labels for few-shot text classification
Timo Schick, Helmut Schmid, and Hinrich Schütze · 2020
Cited alongside, same era.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, T. J. Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeff Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
Cited alongside, same era.
Norbert: Transfer learning for requirements classification
Tobias Hey, Jan Keim, Anne Koziolek, and Walter F. Tichy · 2020
Cited alongside, same era.
Ai chains: Transparent and controllable human-ai interaction by chaining large language model prompts
Tongshuang Wu, Michael Terry, and Carrie Jun Cai · 2022
Later among the works it cites.
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
Later among the works it cites.
Prcbert: Prompt learning for requirement classification using bert-based pretrained language models
Xianchang Luo, Yinxing Xue, Zhenchang Xing, and Jiamou Sun · 2022
Later among the works it cites.
Prompt-tuned code language model as a neural knowledge base for type inference in statically-typed partial code
Qing Huang, Zhiqiang Yuan, Zhenchang Xing, Xiwei Xu, Liming Zhu, and Qinghua Lu · 2022
Later among the works it cites.
Se factual knowledge in frozen giant code model: A study on fqn and its retrieval
Qing Huang, Dianshu Liao, Zhenchang Xing, Zhiqiang Yuan, Qinghua Lu, Xiwei Xu, and Jiaxing Lu · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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
Cited alongside, same era.
What do pre-trained code models know about code?
Anjan Karmakar and Romain Robbes · 2021
Cited alongside, same era.
Treebert: A tree-based pre-trained model for programming language
Xue Jiang, Zhuoran Zheng, Chen Lyu, Liang Li, and Lei Lyu · 2021
Cited alongside, same era.
Yue Wang, Weishi Wang, Shafiq Joty, and Steven CH Hoi · 2021
Cited alongside, same era.
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
Cited alongside, same era.
Adversarial robustness of deep code comment generation
Yu Zhou, Xiaoqing Zhang, Juanjuan Shen, Tingting Han, Taolue Chen, and Harald C. Gall · 2021
Cited alongside, same era.
What do they capture? a structural analysis of pre-trained language models for source code
Yao Wan, Wei Zhao, Hongyu Zhang, Yulei Sui, Guandong Xu, and Hai Jin · 2022
Cited alongside, same era.
Synchromesh: Reliable code generation from pre-trained language models
Gabriel Poesia, Oleksandr Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, and Sumit Gulwani · 2022
Cited alongside, same era.
Later among the works it cites.
An extractive-and-abstractive framework for source code summarization
Weisong Sun, Chunrong Fang, Yuchen Chen, Quanjun Zhang, Guanhong Tao, Tingxu Han, Yifei Ge, Yudu You, and Bin Luo · 2022
Later among the works it cites.
Source code summarization with structural relative position guided transformer
Zi Gong, Cuiyun Gao, Yasheng Wang, Wenchao Gu, Yun Peng, and Zenglin Xu · 2022
Later among the works it cites.
Asleep at the keyboard? assessing the security of github copilot’s code contributions
Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, and Ramesh Karri · 2022
Later among the works it cites.
Codet: Code generation with generated tests
Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang Lou, and Weizhu Chen · 2022
Later among the works it cites.
Fill in the blank: Context-aware automated text input generation for mobile gui testing
Zhe Liu, Chunyang Chen, Junjie Wang, Xing Che, Yuekai Huang, Jun Hu, and Qing Wang · 2022
Later among the works it cites.
Hai Dang, Lukas Mecke, Florian Lehmann, Sven Goller, and Daniel Buschek · 2022
Later among the works it cites.
Openai gpt-3.5
OpenAI · 2023
Closest in time.
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2023
Closest in time.
Api entity and relation joint extraction from text via dynamic prompt-tuned language model
Qing Huang, Yanbang Sun, Zhenchang Xing, Mingming Yu, Xiwei Xu, and Qinghua Lu · 2023
Closest in time.
Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung · 2023
Closest in time.
Capabilities of gpt-4 on medical challenge problems
Harsha Nori, Nicholas King, Scott Mayer McKinney, Dean Carignan, and Eric Horvitz · 2023
Closest in time.
Qing Lyu, Josh Tan, Mike E Zapadka, Janardhana Ponnatapuram, Chuang Niu, Ge Wang, and Christopher T Whitlow · 2023
Closest in time.
Openai chatgpt
OpenAI · 2023
Closest in time.
On the robustness of code generation techniques: An empirical study on github copilot
Antonio Mastropaolo, Luca Pascarella, Emanuela Guglielmi, Matteo Ciniselli, Simone Scalabrino, Rocco Oliveto, and Gabriele Bavota · 2023
Closest in time.