Fetching the paper…
Reading the bibliography…
In the modern era where software plays a pivotal role, software security and vulnerability analysis are essential for secure software development.
1998
Earlier work this paper cites.
M. Sokolova, N. Japkowicz, and S. Szpakowicz, “Beyond accuracy, f-score and roc: a family of discriminant measures for performance evaluation,” in Australasian joint conference on artificial intelligence . Springer, 2006, pp. 1015–1021
2006
Earlier work this paper cites.
M. E. Khan and F. Khan, “A comparative study of white box, black box and grey box testing techniques,” International Journal of Advanced Computer Science and Applications , vol. 3, no. 6, 2012
2012
Earlier work this paper cites.
B. Romera-Paredes and P. Torr, “An embarrassingly simple approach to zero-shot learning,” in Proceedings of the 32nd International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, F. Bach and D. Blei, Eds., vol. 37. Lille, France: PMLR, 07–09 Jul 2015, pp. 2152–2161. [Online]. Available: https://proceedings.mlr.press/v37/romera-paredes15.html
2015
Earlier work this paper cites.
M. Böhme, V.-T. Pham, and A. Roychoudhury, “Coverage-based greybox fuzzing as markov chain,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security , 2016, pp. 1032–1043
2016
Earlier work this paper cites.
M. Böhme, V.-T. Pham, M.-D. Nguyen, and A. Roychoudhury, “Directed greybox fuzzing,” in Proceedings of the 2017 ACM SIGSAC conference on computer and communications security , 2017, pp. 2329–2344
2017
Earlier work this paper cites.
C. Chen, B. Cui, J. Ma, R. Wu, J. Guo, and W. Liu, “A systematic review of fuzzing techniques,” Computers & Security , vol. 75, pp. 118–137, 2018. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0167404818300658
2018
Earlier work this paper cites.
J. Li, B. Zhao, and C. Zhang, “Fuzzing: a survey,” Cybersecurity , vol. 1, pp. 1–13, 2018
2018
Earlier work this paper cites.
H. Liang, X. Pei, X. Jia, W. Shen, and J. Zhang, “Fuzzing: State of the art,” IEEE Transactions on Reliability , vol. 67, no. 3, pp. 1199–1218, 2018
2018
Earlier work this paper cites.
G. Klees, A. Ruef, B. Cooper, S. Wei, and M. Hicks, “Evaluating fuzz testing,” 2018
2018
Earlier work this paper cites.
C. Lemieux and K. Sen, “Fairfuzz: A targeted mutation strategy for increasing greybox fuzz testing coverage,” in 2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE) , 2018, pp. 475–485
2018
Earlier work this paper cites.
J. Wang, B. Chen, L. Wei, and Y. Liu, “Superion: Grammar-aware greybox fuzzing,” in 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) , 2019, pp. 724–735
2019
Earlier work this paper cites.
H. M. Pandey and D. Windridge, “A comprehensive classification of deep learning libraries,” in Third International Congress on Information and Communication Technology: ICICT 2018, London . Springer, 2019, pp. 427–435
2019
Earlier work this paper cites.
F. Petroni, T. Rocktäschel, P. Lewis, A. Bakhtin, Y. Wu, A. H. Miller, and S. Riedel, “Language models as knowledge bases?” 2019
2019
Earlier work this paper cites.
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” 2020
2020
Earlier work this paper cites.
Y. Wang, Q. Yao, J. T. Kwok, and L. M. Ni, “Generalizing from a few examples: A survey on few-shot learning,” ACM computing surveys (csur) , vol. 53, no. 3, pp. 1–34, 2020
2020
Earlier work this paper cites.
Z. Wang, M. Yan, J. Chen, S. Liu, and D. Zhang, “Deep learning library testing via effective model generation,” in Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2020, pp. 788–799
2020
Earlier work this paper cites.
V.-T. Pham, M. Böhme, and A. Roychoudhury, “Aflnet: a greybox fuzzer for network protocols,” in 2020 IEEE 13th International Conference on Software Testing, Validation and Verification (ICST) . IEEE, 2020, pp. 460–465
2020
Earlier work this paper cites.
Y. Lin, Y. Guan, A. Asudeh, and H. Jagadish, “Identifying insufficient data coverage in databases with multiple relations,” Proceedings of the VLDB Endowment , vol. 13, no. 11, 2020
2020
Earlier work this paper cites.
V. J. Manès, H. Han, C. Han, S. K. Cha, M. Egele, E. J. Schwartz, and M. Woo, “The art, science, and engineering of fuzzing: A survey,” IEEE Transactions on Software Engineering , vol. 47, no. 11, pp. 2312–2331, 2021
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
M. Chen, J. Tworek, H. Jun, Q. Yuan et al. , “Evaluating large language models trained on code,” 2021
2021
Earlier work this paper cites.
R. Natella and V.-T. Pham, “Profuzzbench: a benchmark for stateful protocol fuzzing,” in Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis , ser. ISSTA 2021. New York, NY, USA: Association for Computing Machinery, 2021, p. 662–665. [Online]. Available: https://doi.org/10.1145/3460319.3469077
2021
Earlier work this paper cites.
J. Metzman, L. Szekeres, L. Simon, R. Sprabery, and A. Arya, “Fuzzbench: an open fuzzer benchmarking platform and service,” in Proceedings of the 29th ACM joint meeting on European software engineering conference and symposium on the foundations of software engineering , 2021, pp. 1393–1403
2021
Earlier work this paper cites.
2022
Earlier work this paper cites.
R. Luo, L. Sun, Y. Xia, T. Qin, S. Zhang, H. Poon, and T.-Y. Liu, “Biogpt: generative pre-trained transformer for biomedical text generation and mining,” Briefings in Bioinformatics , vol. 23, no. 6, Sep. 2022. [Online]. Available: http://dx.doi.org/10.1093/bib/bbac409
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
2022
Cited alongside, same era.
X. Zhu, S. Wen, S. Camtepe, and Y. Xiang, “Fuzzing: A survey for roadmap,” ACM Comput. Surv. , vol. 54, no. 11s, Sep. 2022. [Online]. Available: https://doi.org/10.1145/3512345
2022
Cited alongside, same era.
2022
Cited alongside, same era.
A. Wei, Y. Deng, C. Yang, and L. Zhang, “Free lunch for testing: Fuzzing deep-learning libraries from open source,” 2022
2022
Cited alongside, same era.
J. Kaddour, J. Harris, M. Mozes, H. Bradley, R. Raileanu, and R. McHardy, “Challenges and applications of large language models,” 2023
2023
Later among the works it cites.
Y. Li, “Business analysis — ai computational cost,” Mar 2023. [Online]. Available: https://medium.com/geekculture/business-analysis-ai-computational-cost-67a136957c95
2023
Later among the works it cites.
2023
Later among the works it cites.
2024
Closest in time.
S. Shetye, “An evaluation of khanmigo, a generative ai tool, as a computer-assisted language learning app,” Studies in Applied Linguistics and TESOL , vol. 24, 07 2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Y. Deng, C. Yang, A. Wei, and L. Zhang, “Fuzzing deep-learning libraries via automated relational api inference,” 2022
2022
Cited alongside, same era.
J. Gu, X. Luo, Y. Zhou, and X. Wang, “Muffin: Testing deep learning libraries via neural architecture fuzzing,” 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Y. Deng, C. S. Xia, H. Peng, C. Yang, and L. Zhang, “Large language models are zero-shot fuzzers: Fuzzing deep-learning libraries via large language models,” 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
H. Naveed, A. U. Khan, S. Qiu, M. Saqib, S. Anwar, M. Usman, N. Akhtar, N. Barnes, and A. Mian, “A comprehensive overview of large language models,” 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Z. Fu, W. Lam, Q. Yu, A. M.-C. So, S. Hu, Z. Liu, and N. Collier, “Decoder-only or encoder-decoder? interpreting language model as a regularized encoder-decoder,” 2023
2023
Cited alongside, same era.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
M. Zalewski, “american fuzzy lop — lcamtuf.coredump.cx,” https://lcamtuf.coredump.cx/afl/
2024
Closest in time.
Y. Jiang, J. Liang, F. Ma, Y. Chen, C. Zhou, Y. Shen, Z. Wu, J. Fu, M. Wang, S. Li, and Q. Zhang, “When fuzzing meets llms: Challenges and opportunities,” in Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering , ser. FSE 2024. New York, NY, USA: Association for Computing Machinery, 2024, p. 492–496. [Online]. Available: https://doi.org/10.1145/3663529.3663784
2024
Closest in time.
R. Meng, M. Mirchev, M. Böhme, and A. Roychoudhury, “Large language model guided protocol fuzzing,” in Proceedings of the 31st Annual Network and Distributed System Security Symposium (NDSS) , 2024
2024
Closest in time.
C. S. Xia, M. Paltenghi, J. L. Tian, M. Pradel, and L. Zhang, “Fuzz4all: Universal fuzzing with large language models,” 2024
2024
Closest in time.
2024
Closest in time.
R. Meng, M. Mirchev, M. Böhme, and A. Roychoudhury, “Large language model guided protocol fuzzing,” in Proceedings of the 31st Annual Network and Distributed System Security Symposium (NDSS) , 2024
2024
Closest in time.
GPF, “Welcome — vdalabs.com,” https://www.vdalabs.com/
2024
Closest in time.
R. Swiecki, “GitHub - google/honggfuzz: Security oriented software fuzzer. Supports evolutionary, feedback-driven fuzzing based on code coverage (SW and HW based) — github.com,” https://github.com/google/honggfuzz
2024
Closest in time.
Google, “AI-Powered Fuzzing: Breaking the Bug Hunting Barrier — security.googleblog.com,” https://security.googleblog.com/2023/08/ai-powered-fuzzing-breaking-bug-hunting.html
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
J. Wang, Y. Huang, C. Chen, Z. Liu, S. Wang, and Q. Wang, “Software testing with large language models: Survey, landscape, and vision,” IEEE Trans. Softw. Eng. , vol. 50, no. 4, p. 911–936, Apr. 2024. [Online]. Available: https://doi.org/10.1109/TSE.2024.3368208
2024
Closest in time.
L. Wang, C. Ma, X. Feng et al. , “A survey on large language model based autonomous agents,” Frontiers of Computer Science , vol. 18, no. 6, Mar. 2024. [Online]. Available: http://dx.doi.org/10.1007/s11704-024-40231-1
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2025
Closest in time.