An improved fuzzing approach based on adaptive random testing

Jinfu Chen, Jingyi Chen, Dong Guo, Dave Towey

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

Fuzzing is a highly automated testing technique. It has been widely used in software vulnerability mining. American fuzzy lop (AFL) is one of the most effective fuzzing tools, with low resource consumption and a variety of efficient fuzzy test strategies. However, because it uses a random testing (RT) algorithm when generating test cases, there is a problem of low quality and low test efficiency. In this paper, we propose an improved fuzzing testing approach based on adaptive random testing (ART) to enhance the effectiveness of AFL test case generation. We also introduce AFL-ART, a new fuzzing tool based on ART. According to the experimental results, AFLART can enhance AFL test case generation, and improve fuzzing testing efficiency.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 31st International Symposium on Software Reliability Engineering Workshops, ISSREW 2020
EditorsMarco Vieira, Henrique Madeira, Nuno Antunes, Zheng Zheng
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages103-108
Number of pages6
ISBN (Electronic)9781728198705
DOIs
Publication statusPublished - Oct 2020
Event31st IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2020 - Virtual, Coimbra, Portugal
Duration: 12 Oct 202015 Oct 2020

Publication series

NameProceedings - 2020 IEEE 31st International Symposium on Software Reliability Engineering Workshops, ISSREW 2020

Conference

Conference31st IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2020
Country/TerritoryPortugal
CityVirtual, Coimbra
Period12/10/2015/10/20

Keywords

  • Adaptive random testing
  • American fuzzy lop
  • Fuzzing
  • Random testing

ASJC Scopus subject areas

  • Software
  • Safety, Risk, Reliability and Quality

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