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SAFL.

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Title: SAFL.
Authors: Wang, Mingzhe, Liang, Jie, Chen, Yuanliang, Jiang, Yu, Jiao, Xun, Liu, Han, Zhao, Xibin, Sun, Jiaguang
Source: ICSE: International Conference on Software Engineering. 5/27/2018, p61-64. 4p.
Subjects: Mutation testing of computer software, Fuzzy logic, Computer software testing, Algorithms, Computer software quality control
Abstract: Mutation-based fuzzing is a widely used software testing technique for bug and vulnerability detection, and the testing performance is greatly affected by the quality of initial seeds and the effectiveness of mutation strategy. In this paper, we present SAFL1, an efficient fuzzing testing tool augmented with qualified seed generation and efficient coverage-directed mutation. First, symbolic execution is used in a lightweight approach to generate qualified initial seeds. Valuable explore directions are learned from the seeds, thus the later fuzzing process can reach deep paths in program state space earlier and easier. Moreover, we implement a fair and fast coverage-directed mutation algorithm. It helps the fuzzing process to exercise rare and deep paths with higher probability. We implement SAFL based on KLEE and AFL and conduct thoroughly repeated evaluations on real-world program benchmarks against state-of-the-art versions of AFL. After 24 hours, compared to AFL and AFLFast, it discovers 214% and 133% more unique crashes, covers 109% and 63% more paths and achieves 279% and 180% more covered branches. Video link: https://youtu.be/LkiFLNMBhVE [ABSTRACT FROM AUTHOR]
Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: SAFL.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Mingzhe%22">Wang, Mingzhe</searchLink><br /><searchLink fieldCode="AR" term="%22Liang%2C+Jie%22">Liang, Jie</searchLink><br /><searchLink fieldCode="AR" term="%22Chen%2C+Yuanliang%22">Chen, Yuanliang</searchLink><br /><searchLink fieldCode="AR" term="%22Jiang%2C+Yu%22">Jiang, Yu</searchLink><br /><searchLink fieldCode="AR" term="%22Jiao%2C+Xun%22">Jiao, Xun</searchLink><br /><searchLink fieldCode="AR" term="%22Liu%2C+Han%22">Liu, Han</searchLink><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Xibin%22">Zhao, Xibin</searchLink><br /><searchLink fieldCode="AR" term="%22Sun%2C+Jiaguang%22">Sun, Jiaguang</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22ICSE%3A+International+Conference+on+Software+Engineering%22">ICSE: International Conference on Software Engineering</searchLink>. 5/27/2018, p61-64. 4p.
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  Data: <searchLink fieldCode="DE" term="%22Mutation+testing+of+computer+software%22">Mutation testing of computer software</searchLink><br /><searchLink fieldCode="DE" term="%22Fuzzy+logic%22">Fuzzy logic</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+testing%22">Computer software testing</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+quality+control%22">Computer software quality control</searchLink>
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  Label: Abstract
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  Data: Mutation-based fuzzing is a widely used software testing technique for bug and vulnerability detection, and the testing performance is greatly affected by the quality of initial seeds and the effectiveness of mutation strategy. In this paper, we present SAFL1, an efficient fuzzing testing tool augmented with qualified seed generation and efficient coverage-directed mutation. First, symbolic execution is used in a lightweight approach to generate qualified initial seeds. Valuable explore directions are learned from the seeds, thus the later fuzzing process can reach deep paths in program state space earlier and easier. Moreover, we implement a fair and fast coverage-directed mutation algorithm. It helps the fuzzing process to exercise rare and deep paths with higher probability. We implement SAFL based on KLEE and AFL and conduct thoroughly repeated evaluations on real-world program benchmarks against state-of-the-art versions of AFL. After 24 hours, compared to AFL and AFLFast, it discovers 214% and 133% more unique crashes, covers 109% and 63% more paths and achieves 279% and 180% more covered branches. Video link: https://youtu.be/LkiFLNMBhVE [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1145/3183440.3183494
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      – Code: eng
        Text: English
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        PageCount: 4
        StartPage: 61
    Subjects:
      – SubjectFull: Mutation testing of computer software
        Type: general
      – SubjectFull: Fuzzy logic
        Type: general
      – SubjectFull: Computer software testing
        Type: general
      – SubjectFull: Algorithms
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      – SubjectFull: Computer software quality control
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      – TitleFull: SAFL.
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            NameFull: Wang, Mingzhe
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            NameFull: Chen, Yuanliang
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            NameFull: Jiang, Yu
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            NameFull: Zhao, Xibin
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          Dates:
            – D: 27
              M: 05
              Text: 5/27/2018
              Type: published
              Y: 2018
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            – TitleFull: ICSE: International Conference on Software Engineering
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