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Large language models (LLMs) have demonstrated significant potential in the realm of natural language understanding and programming code processing tasks.
A synthetic benchmark
Harold J Curnow and Brian A. Wichmann · 1976
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Dhrystone: a synthetic systems programming benchmark
Reinhold P Weicker · 1984
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Class-based n-gram models of natural language
Peter F Brown, Vincent J Della Pietra, Peter V Desouza, Jennifer C Lai, and Robert L Mercer · 1992
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Hint: A new way to measure computer performance
John L Gustafson and Quinn O Snell · 1995
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Memory bandwidth and machine balance in current high performance computers
John D McCalpin et al · 1995
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Static analysis of executables to detect malicious patterns
Mihai Christodorescu and Somesh Jha · 2003
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The linpack benchmark: past, present and future
Jack J Dongarra, Piotr Luszczek, and Antoine Petitet · 2003
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Understanding source code evolution using abstract syntax tree matching
Iulian Neamtiu, Jeffrey S Foster, and Michael Hicks · 2005
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Static detection of security vulnerabilities in scripting languages
Yichen Xie and Alex Aiken · 2006
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Source code analysis: A road map
David Binkley · 2007
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Obfuscating c++ programs via control flow flattening
Tımea László and Ákos Kiss · 2009
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Zozzle: Low-overhead mostly static javascript malware detection
Charles Curtsinger Benjamin Livshits, Ben Zorn, and Christian Seifert · 2010
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Achievements and challenges in software reverse engineering
Gerardo Canfora, Massimiliano Di Penta, and Luigi Cerulo · 2011
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The power of obfuscation techniques in malicious javascript code: A measurement study
Wei Xu, Fangfang Zhang, and Sencun Zhu · 2012
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Are students representatives of professionals in software engineering experiments?
Iflaah Salman, Ayse Tosun Misirli, and Natalia Juristo · 2015
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https://github.com/microsounds/tripforce , 2016
tripforce · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Convolutional neural networks over tree structures for programming language processing
Lili Mou, Ge Li, Lu Zhang, Tao Wang, and Zhi Jin · 2016
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Ransomware cyber attack recap: Nissan confirm they have been hit by hack which crippled NHS
ChronicleLive · 2017
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Bringing the web up to speed with webassembly
Andreas Haas, Andreas Rossberg, Derek L Schuff, Ben L Titzer, Michael Holman, Dan Gohman, Luke Wagner, Alon Zakai, and JF Bastien · 2017
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Wannacrypt ransomware worm targets out-of-date systems
Microsoft · 2017
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A brief study of wannacry threat: Ransomware attack 2017
Savita Mohurle and Manisha Patil · 2017
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What is wannacry/wanacrypt0r?, 2017
National Cybersecurity and Communications Integration Center · 2017
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NHS cyberattack: List of hospitals hit by ransomware strike
Sky News · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Seq2sql: Generating structured queries from natural language using reinforcement learning
Victor Zhong, Caiming Xiong, and Richard Socher · 2017
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The dynamic analysis of wannacry ransomware
Da-Yu Kao and Shou-Ching Hsiao · 2018
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Tsmc chip maker blames wannacry malware for production halt
The Hawker News · 2018
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Manufacturing resilient bi-opaque predicates against symbolic execution
Hui Xu, Yangfan Zhou, Yu Kang, Fengzhi Tu, and Michael Lyu · 2018
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https://github.com/AndriyMulyar/semantic-text-similarity , 2019
semantic-text-similarity · 2019
Cited alongside, same era.
Finbert: Financial sentiment analysis with pre-trained language models
Dogu Araci · 2019
Cited alongside, same era.
Codesearchnet challenge: Evaluating the state of semantic code search
Hamel Husain, Ho-Hsiang Wu, Tiferet Gazit, Miltiadis Allamanis, and Marc Brockschmidt · 2019
Cited alongside, same era.
Release strategies and the social impacts of language models
Irene Solaiman, Miles Brundage, Jack Clark, Amanda Askell, Ariel Herbert-Voss, Jeff Wu, Alec Radford, Gretchen Krueger, Jong Wook Kim, Sarah Kreps, et al · 2019
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High-performance medicine: the convergence of human and artificial intelligence
Eric J Topol · 2019
Grounded Copilot: How Programmers Interact with Code-Generating Models
Shraddha Barke, Michael B. James, and Nadia Polikarpova · 2023
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Identifying and mitigating the security risks of generative ai
Clark Barrett, Brad Boyd, Ellie Burzstein, Nicholas Carlini, Brad Chen, Jihye Choi, Amrita Roy Chowdhury, Mihai Christodorescu, Anupam Datta, Soheil Feizi, et al · 2023
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Taking Flight with Copilot
Christian Bird, Denae Ford, Thomas Zimmermann, Nicole Forsgren, Eirini Kalliamvakou, Travis Lowdermilk, and Idan Gazit · 2023
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Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al · 2023
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Evaluating the feasibility of chatgpt in healthcare: an analysis of multiple clinical and research scenarios
Marco Cascella, Jonathan Montomoli, Valentina Bellini, and Elena Bignami · 2023
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Cited alongside, same era.
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
Cited alongside, same era.
Efficient vulnerability detection based on abstract syntax tree and deep learning
Hantao Feng, Xiaotong Fu, Hongyu Sun, He Wang, and Yuqing Zhang · 2020
Cited alongside, same era.
Bgnn4vd: Constructing bidirectional graph neural-network for vulnerability detection
Sicong Cao, Xiaobing Sun, Lili Bo, Ying Wei, and Bin Li · 2021
Cited alongside, same era.
Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Software obfuscation with non-linear mixed boolean-arithmetic expressions
Binbin Liu, Weijie Feng, Qilong Zheng, Jing Li, and Dongpeng Xu · 2021
Cited alongside, same era.
https://github.com/jaiimeriios/js-apps , 2022
js-apps · 2022
Cited alongside, same era.
Closest in time.
Can large language models be an alternative to human evaluations?
Cheng-Han Chiang and Hung-yi Lee · 2023
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Github copilot
Github · 2023
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Submission and peer review policies
IEEE · 2023
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Chit-chat or deep talk: Prompt engineering for process mining
Urszula Jessen, Michal Sroka, and Dirk Fahland · 2023
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Chatgpt for good? on opportunities and challenges of large language models for education
Enkelejda Kasneci, Kathrin Seßler, Stefan Küchemann, Maria Bannert, Daryna Dementieva, Frank Fischer, Urs Gasser, Georg Groh, Stephan Günnemann, Eyke Hüllermeier, et al · 2023
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Comparing Code Explanations Created by Students and Large Language Models, April 2023
Juho Leinonen, Paul Denny, Stephen MacNeil, Sami Sarsa, Seth Bernstein, Joanne Kim, Andrew Tran, and Arto Hellas · 2023
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Chatgpt can now see, hear, and speak
OpenAI · 2023
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Gpt-4 technical report
OpenAI · 2023
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The Robots are Here: Navigating the Generative AI Revolution in Computing Education
James Prather, Paul Denny, Juho Leinonen, Brett A. Becker, Ibrahim Albluwi, Michelle Craig, Hieke Keuning, Natalie Kiesler, Tobias Kohn, Andrew Luxton-Reilly, Stephen MacNeil, Andrew Peterson, Raymond Pettit, Brent N. Reeves, and Jaromir Savelka · 2023
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Code llama: Open foundation models for code
Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, et al · 2023
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Gpt-4 is here: what scientists think
Katharine Sanderson · 2023
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Alpaca: A strong, replicable instruction-following model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B Hashimoto · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Creating a coding assistant with starcoder
Lewis Tunstall, Nathan Lambert, Nazneen Rajani, Edward Beeching, Teven Le Scao, Leandro von Werra, Sheon Han, Philipp Schmid, and Alexander Rush · 2023
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Copiloting the Copilots: Fusing Large Language Models with Completion Engines for Automated Program Repair
Yuxiang Wei, Chunqiu Steven Xia, and Lingming Zhang · 2023
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Conversational Automated Program Repair, January 2023
Chunqiu Steven Xia and Lingming Zhang · 2023
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Lmpa: Improving decompilation by synergy of large language model and program analysis
Xiangzhe Xu, Zhuo Zhang, Shiwei Feng, Yapeng Ye, Zian Su, Nan Jiang, Siyuan Cheng, Lin Tan, and Xiangyu Zhang · 2023
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Evaluating instruction-tuned large language models on code comprehension and generation
Zhiqiang Yuan, Junwei Liu, Qiancheng Zi, Mingwei Liu, Xin Peng, and Yiling Lou · 2023
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Privee: A wearable for real-time bladder monitoring system
Ruoyu Zhang, Ruijie Fang, Chongzhou Fang, Houman Homayoun, and Gozde Goncu Berk · 2023
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A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al · 2023
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Enhancing LLM-Based Coding Tools through Native Integration of IDE-Derived Static Context, February 2024
Yichen Li, Yun Peng, Yintong Huo, and Michael R. Lyu · 2024
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Interactions with Prompt Problems: A New Way to Teach Programming with Large Language Models, January 2024
James Prather, Paul Denny, Juho Leinonen, David H. Smith IV, Brent N. Reeves, Stephen MacNeil, Brett A. Becker, Andrew Luxton-Reilly, Thezyrie Amarouche, and Bailey Kimmel · 2024
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