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[BohmeF20a]
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Marcel Böhme and Brandon Falk. Boosting fuzzer efficiency: an information theoretic perspective. In Proceedings of the ACM European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) 2020. Association for Computing Machinery, 2020. URL: https://mboehme.github.io/paper/FSE20.Entropy.pdf.
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[BohmeF20b]
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(1,2)
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Marcel Böhme and Brandon Falk. Fuzzing: on the exponential cost of vulnerability discovery. In Proceedings of the ACM European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) 2020. Association for Computing Machinery, 2020. URL: https://mboehme.github.io/paper/FSE20.EmpiricalLaw.pdf.
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[CGZ+13]
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Yang Chen, Alex Groce, Chaoqiang Zhang, Weng-Keen Wong, Xiaoli Fern, Eric Eide, and John Regehr. Taming compiler fuzzers. In Proceedings of the 34th ACM SIGPLAN Conference on Programming Language Design and Implementation, PLDI '13, 197–208. New York, NY, USA, 2013. Association for Computing Machinery. URL: http://www.cs.utah.edu/~regehr/papers/pldi13.pdf, doi:10.1145/2491956.2462173.
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[GAG14]
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Alex Groce, Mohammad Amin Alipour, and Rahul Gopinath. Coverage and its discontents. In Proceedings of the 2014 ACM International Symposium on New Ideas, New Paradigms, and Reflections on Programming & Software, Onward! 2014, 255–268. New York, NY, USA, 2014. Association for Computing Machinery. URL: https://agroce.github.io/onwardessays14.pdf, doi:10.1145/2661136.2661157.
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[GZE+12]
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Alex Groce, Chaoqiang Zhang, Eric Eide, Yang Chen, and John Regehr. Swarm testing. In Proceedings of the 2012 International Symposium on Software Testing and Analysis, ISSTA 2012, 78–88. New York, NY, USA, 2012. Association for Computing Machinery. URL: https://www.cs.utah.edu/~regehr/papers/swarm12.pdf, doi:10.1145/2338965.2336763.
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[HGH+19]
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Adrian Herrera, Hendra Gunadi, Liam Hayes, Shane Magrath, Felix Friedlander, Maggi Sebastian, Michael Norrish, and Antony L. Hosking. Corpus distillation for effective fuzzing: a comparative evaluation. 2019. arXiv:1905.13055.
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[KRC+18]
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George Klees, Andrew Ruef, Benji Cooper, Shiyi Wei, and Michael Hicks. Evaluating fuzz testing. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, CCS '18, 2123–2138. New York, NY, USA, 2018. Association for Computing Machinery. URL: https://www.cs.umd.edu/~mwh/papers/fuzzeval.pdf, doi:10.1145/3243734.3243804.
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[LS18]
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Caroline Lemieux and Koushik Sen. Fairfuzz: a targeted mutation strategy for increasing greybox fuzz testing coverage. In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, ASE 2018, 475–485. New York, NY, USA, 2018. Association for Computing Machinery. URL: https://www.carolemieux.com/fairfuzz-ase18.pdf, doi:10.1145/3238147.3238176.
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[LJC+18]
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Jie Liang, Yu Jiang, Yuanliang Chen, Mingzhe Wang, Chijin Zhou, and Jiaguang Sun. Pafl: extend fuzzing optimizations of single mode to industrial parallel mode. In Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2018, 809–814. New York, NY, USA, 2018. Association for Computing Machinery. URL: http://wingtecher.com/themes/WingTecherResearch/assets/papers/fse18-pafl.pdf, doi:10.1145/3236024.3275525.
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[LJZ+19]
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Chenyang Lyu, Shouling Ji, Chao Zhang, Yuwei Li, Wei-Han Lee, Yu Song, and Raheem Beyah. MOPT: optimized mutation scheduling for fuzzers. In 28th USENIX Security Symposium (USENIX Security 19), 1949–1966. Santa Clara, CA, August 2019. USENIX Association. URL: https://www.usenix.org/system/files/sec19-lyu.pdf.
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[MD20]
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David R. MacIver and Alastair F. Donaldson. Test-Case Reduction via Test-Case Generation: Insights from the Hypothesis Reducer (Tool Insights Paper). In Robert Hirschfeld and Tobias Pape, editors, 34th European Conference on Object-Oriented Programming (ECOOP 2020), volume 166 of Leibniz International Proceedings in Informatics (LIPIcs), 13:1–13:27. Dagstuhl, Germany, 2020. Schloss Dagstuhl–Leibniz-Zentrum für Informatik. URL: https://drops.dagstuhl.de/opus/volltexte/2020/13170, doi:10.4230/LIPIcs.ECOOP.2020.13.
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[MZH20]
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Barton P. Miller, Mengxiao Zhang, and Elisa R. Heymann. The relevance of classic fuzz testing: have we solved this one? 2020. arXiv:2008.06537.
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[NH18]
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Stefan Nagy and Matthew Hicks. Full-speed fuzzing: reducing fuzzing overhead through coverage-guided tracing. 2018. arXiv:1812.11875.
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[NG22]
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Hoang Lam Nguyen and Lars Grunske. Bedivfuzz: integrating behavioral diversity into generator-based fuzzing. 2022. arXiv:2202.13114.
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[RLPS20]
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Sameer Reddy, Caroline Lemieux, Rohan Padhye, and Koushik Sen. Quickly generating diverse valid test inputs with reinforcement learning. In Proceedings of the 42st International Conference on Software Engineering. IEEE Press, 2020. URL: https://www.carolemieux.com/rlcheck_preprint.pdf.
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[RZD+21]
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Nicola Ruaro, Kyle Zeng, Lukas Dresel, Mario Polino, Tiffany Bao, Andrea Continella, Stefano Zanero, Christopher Kruegel, and Giovanni Vigna. SyML: Guiding Symbolic Execution Toward Vulnerable States Through Pattern Learning, pages 456–468. Association for Computing Machinery, New York, NY, USA, 2021. URL: https://conand.me/publications/ruaro-syml-2021.pdf.
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[VCM18]
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Vassilis Vassiliades, Konstantinos Chatzilygeroudis, and Jean-Baptiste Mouret. Using centroidal voronoi tessellations to scale up the multidimensional archive of phenotypic elites algorithm. IEEE Transactions on Evolutionary Computation, 22(4):623–630, 2018. doi:10.1109/TEVC.2017.2735550.
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[WDS+19]
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Jinghan Wang, Yue Duan, Wei Song, Heng Yin, and Chengyu Song. Be sensitive and collaborative: analyzing impact of coverage metrics in greybox fuzzing. In 22nd International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2019), 1–15. Chaoyang District, Beijing, September 2019. USENIX Association. URL: https://www.usenix.org/conference/raid2019/presentation/wang.
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[WZL+20]
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Pengfei Wang, Xu Zhou, Kai Lu, Yingying Liu, and Tai Yue. Sok: the progress, challenges, and perspectives of directed greybox fuzzing. 2020. arXiv:2005.11907.
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[WLR20]
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(1,2)
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Zi Wang, Ben Liblit, and Thomas Reps. Tofu: target-oriented fuzzer. 2020. arXiv:2004.14375.
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[ZGBohme+19]
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Andreas Zeller, Rahul Gopinath, Marcel Böhme, Gordon Fraser, and Christian Holler. The fuzzing book. In The Fuzzing Book. Saarland University, 2019. URL: https://www.fuzzingbook.org/ (visited on 2019-09-09 16:42:54+02:00).
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