Novelty Not Found: Exploring Input Shadowing in Fuzzing through Adaptive Fuzzer Restarts—RCR Report
This is the Replicated Computational Results (RCR) Report for our ACM TOSEM paper, “Novelty Not Found: Exploring Input Shadowing in Fuzzing through Adaptive Fuzzer Restarts”. In this paper, we demonstrate how input shadowing can impact the fuzzing process and propose a mitigation strategy: restarting the fuzzing process when progress stagnates. As part of this RCR, we provide a replication package to facilitate the reproduction of our results.
Authors
- Nils Bars (ORCID: https://orcid.org/0009-0001-5179-4002)
- Thorsten Holz (ORCID: https://orcid.org/0000-0002-2783-1264)
- Lukas Bernhard (ORCID: https://orcid.org/0009-0005-8564-1476)
- Moritz Schloegel (ORCID: https://orcid.org/0000-0003-1630-1687)
- Nico Schiller (ORCID: https://orcid.org/0009-0004-6401-5989)
- Xinyi Xu (ORCID: https://orcid.org/0000-0002-5378-959X)
Institutions
- Helmholtz Center for Information Security (DE)
Publication Details
- Journal
- ACM Transactions on Software Engineering and Methodology
- Published
- 2025-01-16
- DOI
- https://doi.org/10.1145/3712590
- Primary Topic
- Mobile Crowdsensing and Crowdsourcing
- Type
- article
- Field-Weighted Citation Impact
- 0.00