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Crossover in Parametric Fuzzing.

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Title: Crossover in Parametric Fuzzing.
Authors: Hough, Katherine1 hough.k@northeastern.edu, Bell, Jonathan1 j.bell@northeastern.edu
Source: ICSE: International Conference on Software Engineering. 2024, p1-12. 12p.
Subjects: Fuzzy sets, Program generators (Computer programs), Operator algebras, Operator theory, Data analysis
Abstract: Parametric fuzzing combines evolutionary and generator-based fuzzing to create structured test inputs that exercise unique execution behaviors. Parametric fuzzers internally represent inputs as bit strings referred to as "parameter sequences". Interesting parameter sequences are saved by the fuzzer and perturbed to create new inputs without the need for type-specific operators. However, existing work on parametric fuzzing only uses mutation operators, which modify a single input; it does not incorporate crossover, an evolutionary operator that blends multiple inputs together. Crossover operators aim to combine advantageous traits from multiple inputs. However, the nature of parametric fuzzing limits the effectiveness of traditional crossover operators. In this paper, we propose linked crossover, an approach for using dynamic execution information to identify and exchange analogous portions of parameter sequences. We created an implementation of linked crossover for Java and evaluated linked crossover's ability to preserve advantageous traits. We also evaluated linked crossover's impact on fuzzer performance on seven real-world Java projects and found that linked crossover consistently performed as well as or better than three state-of-the-art parametric fuzzers and two other forms of crossover on both long and short fuzzing campaigns. [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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An: 185196440
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  Data: Crossover in Parametric Fuzzing.
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  Data: <searchLink fieldCode="AR" term="%22Hough%2C+Katherine%22">Hough, Katherine</searchLink><relatesTo>1</relatesTo><i> hough.k@northeastern.edu</i><br /><searchLink fieldCode="AR" term="%22Bell%2C+Jonathan%22">Bell, Jonathan</searchLink><relatesTo>1</relatesTo><i> j.bell@northeastern.edu</i>
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  Data: <searchLink fieldCode="DE" term="%22Fuzzy+sets%22">Fuzzy sets</searchLink><br /><searchLink fieldCode="DE" term="%22Program+generators+%28Computer+programs%29%22">Program generators (Computer programs)</searchLink><br /><searchLink fieldCode="DE" term="%22Operator+algebras%22">Operator algebras</searchLink><br /><searchLink fieldCode="DE" term="%22Operator+theory%22">Operator theory</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink>
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  Data: Parametric fuzzing combines evolutionary and generator-based fuzzing to create structured test inputs that exercise unique execution behaviors. Parametric fuzzers internally represent inputs as bit strings referred to as "parameter sequences". Interesting parameter sequences are saved by the fuzzer and perturbed to create new inputs without the need for type-specific operators. However, existing work on parametric fuzzing only uses mutation operators, which modify a single input; it does not incorporate crossover, an evolutionary operator that blends multiple inputs together. Crossover operators aim to combine advantageous traits from multiple inputs. However, the nature of parametric fuzzing limits the effectiveness of traditional crossover operators. In this paper, we propose linked crossover, an approach for using dynamic execution information to identify and exchange analogous portions of parameter sequences. We created an implementation of linked crossover for Java and evaluated linked crossover's ability to preserve advantageous traits. We also evaluated linked crossover's impact on fuzzer performance on seven real-world Java projects and found that linked crossover consistently performed as well as or better than three state-of-the-art parametric fuzzers and two other forms of crossover on both long and short fuzzing campaigns. [ABSTRACT FROM AUTHOR]
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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:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1145/3597503.3639160
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 1
    Subjects:
      – SubjectFull: Fuzzy sets
        Type: general
      – SubjectFull: Program generators (Computer programs)
        Type: general
      – SubjectFull: Operator algebras
        Type: general
      – SubjectFull: Operator theory
        Type: general
      – SubjectFull: Data analysis
        Type: general
    Titles:
      – TitleFull: Crossover in Parametric Fuzzing.
        Type: main
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            NameFull: Hough, Katherine
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            NameFull: Bell, Jonathan
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          Dates:
            – D: 01
              M: 05
              Text: 2024
              Type: published
              Y: 2024
          Titles:
            – TitleFull: ICSE: International Conference on Software Engineering
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