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Genealogy Database of Fuzzers

This repository is our attempt to maintain an up-to-date genealogy database of fuzzers and relevant papers. It is the continuation of an initial effort made by Manès et al. in "The Art, Science, and Engineering of Fuzzing: A Survey", published in 2019 in IEEE Transactions on Software Engineering. You can visit https://fuzzing-survey.org to see an interactive site backed by this database.

What is this survey about?

Our survey is about fuzzers and the relevant literature. Since "fuzzing" is a largely overloaded term, a primary goal of our survey is to precisely define what fuzzing is and to characterize various fuzzers. To this end, we split the process of fuzzing into several steps and use them to systematically categorize fuzzers based on their features. This repository maintains one of the major outcomes of this effort, namely a genealogy graph of fuzzers.

How is this genealogy graph rendered?

We use a force-directed graph layout algorithm with several tweaks. In our current layout, nodes tend to be sorted vertically based on their year of publication and inter-linked nodes tend to be spatially clustered together.

How can I contribute?

We have seeded this repository with the data we collected for our 2019 survey. Due to the rapid development in fuzzing, we realize our database will quickly become outdated due to missing papers and tools. It is our hope that, by hosting this repository in public, you can contribute to this database and help keep it up-to-date. Please proceed to the contribution guideline if you wish to contribute.

Who are the maintainers of this database?

This database is currently maintained by:

How do I cite this work?

If you plan to refer to this work, please consider citing our 2019 survey using the following BibTeX entry. Thank you!

(We are hosting a pre-print of our survey until the final version is published at IEEE.)

@ARTICLE{manes:tse:2021,
  author = {Valentin J. M. Man{\`{e}}s and HyungSeok Han and Choongwoo Han and Sang Kil Cha and Manuel Egele and Edward J. Schwartz and Maverick Woo},
  title = {The Art, Science, and Engineering of Fuzzing: A Survey},
  journal = {IEEE Transactions on Software Engineering},
  volume = {47},
  number = {11},
  pages = {2312--2331},
  year = 2021
}

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