@inproceedings{330dd49e23f646c383c8dbaa1d3d579c,
title = "An improved fuzzing approach based on adaptive random testing",
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.",
keywords = "Adaptive random testing, American fuzzy lop, Fuzzing, Random testing",
author = "Jinfu Chen and Jingyi Chen and Dong Guo and Dave Towey",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 31st IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2020 ; Conference date: 12-10-2020 Through 15-10-2020",
year = "2020",
month = oct,
doi = "10.1109/ISSREW51248.2020.00045",
language = "English",
series = "Proceedings - 2020 IEEE 31st International Symposium on Software Reliability Engineering Workshops, ISSREW 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "103--108",
editor = "Marco Vieira and Henrique Madeira and Nuno Antunes and Zheng Zheng",
booktitle = "Proceedings - 2020 IEEE 31st International Symposium on Software Reliability Engineering Workshops, ISSREW 2020",
address = "United States",
}