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Fuzzing has achieved tremendous success in discovering bugs and vulnerabilities in various software systems.
The Curious Case of Neural Text Degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019 · 1904
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Fine-Tuning Language Models from Human Preferences
Daniel M. Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B. Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. 2019 · 1909
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On a test of whether one of two random variables is stochastically larger than the other
Henry B Mann and Donald R Whitney. 1947 · 1947
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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 · 2002
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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, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey 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 · 2005
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Introducing jsfunfuzz
jsfunfuzz 2017 · 2007
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Fuzzing: Brute Force Vulnerability Discovery
Michael Sutton, Adam Greene, and Pedram Amini. 2007 · 2007
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Finding and understanding bugs in C compilers. In Proceedings of the 32nd ACM SIGPLAN conference on Programming language design and implementation . 283–294
Xuejun Yang, Yang Chen, Eric Eide, and John Regehr. 2011 · 2011
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Fuzzing with code fragments. In 21st USENIX Security Symposium (USENIX Security 12) . 445–458
Christian Holler, Kim Herzig, and Andreas Zeller. 2012 · 2012
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Many-core compiler fuzzing
Christopher Lidbury, Andrei Lascu, Nathan Chong, and Alastair F Donaldson. 2015 · 2015
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American Fuzzy Lop - Whitepaper
M. Zalewski 2016 · 2016
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Learning to fuzz: Application-independent fuzz testing with probabilistic, generative models of input data
Jibesh Patra and Michael Pradel. 2016 · 2016
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Open Quantum Assembly Language
Andrew W. Cross, Lev S. Bishop, John A. Smolin, and Jay M. Gambetta. 2017 · 2017
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Learn&fuzz: Machine learning for input fuzzing. In 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 50–59
Patrice Godefroid, Hila Peleg, and Rishabh Singh. 2017 · 2017
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Program synthesis
Sumit Gulwani, Oleksandr Polozov, Rishabh Singh, et al · 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 · 2017
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Compiler fuzzing through deep learning. In Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis . 95–105
Chris Cummins, Pavlos Petoumenos, Alastair Murray, and Hugh Leather. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Open Source Software in Quantum Computing
Mark Fingerhuth, Tomáš Babej, and Peter Wittek. 2018 · 2018
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Evaluating Fuzz Testing. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (CCS ’18) . Association for Computing Machinery, New York, NY, USA, 2123–2138
George Klees, Andrew Ruef, Benji Cooper, Shiyi Wei, and Michael Hicks. 2018 · 2018
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Feedback-directed differential testing of interactive debuggers. In ESEC/SIGSOFT FSE . 610–620
Daniel Lehmann and Michael Pradel. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al · 2018
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NAUTILUS: Fishing for Deep Bugs with Grammars.. In NDSS
Cornelius Aschermann, Tommaso Frassetto, Thorsten Holz, Patrick Jauernig, Ahmad-Reza Sadeghi, and Daniel Teuchert. 2019 · 2019
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Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 2019
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Deepfuzz: Automatic generation of syntax valid c programs for fuzz testing. In Proceedings of the AAAI Conference on Artificial Intelligence , Vol. 33. 1044–1051
Xiao Liu, Xiaoting Li, Rupesh Prajapati, and Dinghao Wu. 2019 · 2019
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The fuzzing book
Andreas Zeller, Rahul Gopinath, Marcel Böhme, Gordon Fraser, and Christian Holler. 2019 · 2019
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SeqFuzzer: An Industrial Protocol Fuzzing Framework from a Deep Learning Perspective. In 2019 12th IEEE Conference on Software Testing, Validation and Verification (ICST) . 59–67
Hui Zhao, Zhihui Li, Hansheng Wei, Jianqi Shi, and Yanhong Huang. 2019 · 2019
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Fuzzing: Challenges and reflections
Marcel Böhme, Cristian Cadar, and Abhik Roychoudhury. 2020 · 2020
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A survey of compiler testing
Junjie Chen, Jibesh Patra, Michael Pradel, Yingfei Xiong, Hongyu Zhang, Dan Hao, and Lu Zhang. 2020 · 2020
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Cudasmith: A fuzzer for CUDA compilers. In 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC) . IEEE, 861–871
Bo Jiang, Xiaoyan Wang, Wing Kwong Chan, TH Tse, Na Li, Yongfeng Yin, and Zhenyu Zhang. 2020 · 2020
Cited alongside, same era.
Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020 · 2020
Cited alongside, same era.
Montage: A Neural Network Language { \{ Model-Guided } \} { \{ JavaScript } \} Engine Fuzzer. In 29th USENIX Security Symposium (USENIX Security 20) . 2613–2630
Suyoung Lee, HyungSeok Han, Sang Kil Cha, and Sooel Son. 2020 · 2020
Cited alongside, same era.
Random testing for C and C++ compilers with YARPGen
Vsevolod Livinskii, Dmitry Babokin, and John Regehr. 2020 · 2020
Cited alongside, same era.
Exploiting cloze questions for few shot text classification and natural language inference
Tf-coder: Program synthesis for tensor manipulations
Kensen Shi, David Bieber, and Rishabh Singh. 2022 · 2022
Later among the works it cites.
No more fine-tuning? an experimental evaluation of prompt tuning in code intelligence. In Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering . 382–394
Chaozheng Wang, Yuanhang Yang, Cuiyun Gao, Yun Peng, Hongyu Zhang, and Michael R Lyu. 2022 · 2022
Later among the works it cites.
Free lunch for testing: Fuzzing deep-learning libraries from open source. In Proceedings of the 44th International Conference on Software Engineering . 995–1007
Anjiang Wei, Yinlin Deng, Chenyuan Yang, and Lingming Zhang. 2022 · 2022
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A Systematic Evaluation of Large Language Models of Code. In Proceedings of the 6th ACM SIGPLAN International Symposium on Machine Programming (San Diego, CA, USA) (MAPS 2022) . Association for Computing Machinery, New York, NY, USA, 1–10
Frank F. Xu, Uri Alon, Graham Neubig, and Vincent Josua Hellendoorn. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Timo Schick and Hinrich Schütze. 2020 · 2020
Cited alongside, same era.
Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2020
Cited alongside, same era.
On the unusual effectiveness of type-aware operator mutations for testing SMT solvers
Dominik Winterer, Chengyu Zhang, and Zhendong Su. 2020a · 2020
Cited alongside, same era.
Qiskit/Qiskit
2021 · 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.
One engine to fuzz’em all: Generic language processor testing with semantic validation. In 2021 IEEE Symposium on Security and Privacy (SP) . IEEE, 642–658
Yongheng Chen, Rui Zhong, Hong Hu, Hangfan Zhang, Yupeng Yang, Dinghao Wu, and Wenke Lee. 2021b · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
Cited alongside, same era.
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2021 · 2021
Cited alongside, same era.
History-Driven Test Program Synthesis for JVM Testing. In Proceedings of the 44th International Conference on Software Engineering (Pittsburgh, Pennsylvania) (ICSE ’22) . 1133–1144
Yingquan Zhao, Zan Wang, Junjie Chen, Mengdi Liu, Mingyuan Wu, Yuqun Zhang, and Lingming Zhang. 2022 · 2022
Later among the works it cites.
Large language models are human-level prompt engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2022 · 2022
Later among the works it cites.
std::expected
2023 · 2023
Closest in time.
Yejin Bang, Samuel Cahyawijaya, Nayeon Lee, Wenliang Dai, Dan Su, Bryan Wilie, Holy Lovenia, Ziwei Ji, Tiezheng Yu, Willy Chung, et al · 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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No Grammar, No Problem: Towards Fuzzing the Linux Kernel without System-Call Descriptions. In Network and Distributed System Security (NDSS) Symposium 2023
Alexander Bulekov, Bandan Das, Stefan Hajnoczi, and Manuel Egele. 2023 · 2023
Closest in time.
Large Language Models are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models. In Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis . 423–435
Yinlin Deng, Chunqiu Steven Xia, Haoran Peng, Chenyuan Yang, and Lingming Zhang. 2023 · 2023
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GrayC: Greybox Fuzzing of Compilers and Analysers for C (ISSTA 2023) . Association for Computing Machinery, New York, NY, USA, 1219–1231
Karine Even-Mendoza, Arindam Sharma, Alastair F. Donaldson, and Cristian Cadar. 2023 · 2023
Closest in time.
go-fuzz: randomized testing for Go
go-fuzz 2023 · 2023
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Models - GPT-4
gpt4endpoint 2023 · 2023
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CODAMOSA: Escaping Coverage Plateaus in Test Generation with Pre-trained Large Language Models. In 45th International Conference on Software Engineering
Caroline Lemieux, Jeevana Priya Inala, Shuvendu K Lahiri, and Siddhartha Sen. 2023 · 2023
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StarCoder: may the source be with you!
Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, et al · 2023
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libFuzzer – a library for coverage-guided fuzz testing
libFuzzer 2023 · 2023
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Nnsmith: Generating diverse and valid test cases for deep learning compilers. In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2 . 530–543
Jiawei Liu, Jinkun Lin, Fabian Ruffy, Cheng Tan, Jinyang Li, Aurojit Panda, and Lingming Zhang. 2023 · 2023
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A Survey of Modern Compiler Fuzzing
Haoyang Ma. 2023 · 2023
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Learning Deep Semantics for Test Completion. In 45th International Conference on Software Engineering
Pengyu Nie, Rahul Banerjee, Junyi Jessy Li, Raymond J. Mooney, and Milos Gligoric. 2023 · 2023
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OpenAI. 2023 · 2023
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MorphQ: Metamorphic Testing of the Qiskit Quantum Computing Platform. In 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE Computer Society, 2413–2424
Matteo Paltenghi and Michael Pradel. 2023 · 2023
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Adaptive Test Generation Using a Large Language Model
Max Schäfer, Sarah Nadi, Aryaz Eghbali, and Frank Tip. 2023 · 2023
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syzkaller - kernel fuzzer
syzkaller 2023 · 2023
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TensorFlow
TensorFlow 2023 · 2023
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Can Large Language Models Write Good Property-Based Tests?
Vasudev Vikram, Caroline Lemieux, and Rohan Padhye. 2023 · 2023
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Keep the Conversation Going: Fixing 162 out of 337 bugs for $0.42 each using ChatGPT
Chunqiu Steven Xia and Lingming Zhang. 2023 · 2023
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No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation
Zhiqiang Yuan, Yiling Lou, Mingwei Liu, Shiji Ding, Kaixin Wang, Yixuan Chen, and Xin Peng. 2023 · 2023
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