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A preliminary taxonomy of techniques used in software fuzzing

Published: 21 September 2020 Publication History

Abstract

Software fuzzing is a testing technique, which generates erroneous and random input to a software so that the software of interest can be monitored for exceptions such as crashes [1]. Both in the open source software (OSS) and proprietary domain, fuzzing has been widely used to explore software vulnerabilities. For example, information technology (IT) organizations such as Google1 and Microsoft2 use software fuzzing as part of the software development process. As of Jan 2019, GitHub hosts 2,915 OSS repositories related to fuzzing3.

References

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Paul Ammann and Jeff Offutt. 2016. Introduction to software testing. Cambridge University Press.
[2]
Stuart Anderson, Pauline Allen, Stephen Peckham, and Nick Goodwin. 2008. Asking the right questions: scoping studies in the commissioning of research on the organisation and delivery of health services. Health research policy and systems 6, 1 (2008), 7.
[3]
Edmund M Clarke and Jeannette M Wing. 1996. Formal methods: State of the art and future directions. ACM Computing Surveys (CSUR) 28, 4 (1996), 626--643.
[4]
Mark Harman. 2007. The current state and future of search based software engineering. In 2007 Future of Software Engineering. IEEE Computer Society, 342--357.
[5]
James C King. 1976. Symbolic execution and program testing. Commun. ACM 19, 7 (1976), 385--394.
[6]
E. S. Mesh and J. S. Hawker. 2013. Scientific software process improvement decisions: A proposed research strategy. In 2013 5th International Workshop on Software Engineering for Computational Science and Engineering (SE-CSE). 32--39.
[7]
Sam Newman. 2015. Building microservices: designing fine-grained systems. "O'Reilly Media, Inc.".
[8]
Akond Rahman and Laurie Williams. 2019. A Bird's Eye View of Knowledge Needs Related to Penetration Testing (HotSoS '19). Association for Computing Machinery, New York, NY, USA, Article Article 9, 2 pages.
[9]
E.J. Schwartz, T. Avgerinos, and D. Brumley. 2010. All You Ever Wanted to Know about Dynamic Taint Analysis and Forward Symbolic Execution (but Might Have Been Afraid to Ask). In 2010 IEEE Symposium on Security and Privacy. 317--331.
[10]
Moshe Y Vardi. 2009. Conferences vs. journals in computing research. Commun. ACM 52, 5 (2009), 5--5.
[11]
T. Zimmermann. 2016. Card-sorting: From text to themes. In Perspectives on Data Science for Software Engineering, Tim Menzies, Laurie Williams, and Thomas Zimmermann (Eds.). Morgan Kaufmann, Boston, 137 -- 141.

Cited By

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  • (2024)Software Vulnerability Fuzz Testing: A Mutation-Selection Optimization Systematic ReviewEngineering, Technology & Applied Science Research10.48084/etasr.697114:4(14961-14969)Online publication date: 2-Aug-2024

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  1. A preliminary taxonomy of techniques used in software fuzzing

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    cover image ACM Other conferences
    HotSoS '20: Proceedings of the 7th Symposium on Hot Topics in the Science of Security
    September 2020
    189 pages
    ISBN:9781450375610
    DOI:10.1145/3384217
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 21 September 2020

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    Author Tags

    1. fuzzing
    2. scoping review
    3. software security
    4. taxonomy

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    HotSoS '20
    HotSoS '20: Hot Topics in the Science of Security
    September 21 - 23, 2020
    Kansas, Lawrence

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    Overall Acceptance Rate 34 of 60 submissions, 57%

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    • (2024)Software Vulnerability Fuzz Testing: A Mutation-Selection Optimization Systematic ReviewEngineering, Technology & Applied Science Research10.48084/etasr.697114:4(14961-14969)Online publication date: 2-Aug-2024

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