REVIEW 2 cited by
A Review of Machine Learning Applications in Fuzzing
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Fuzzing has played an important role in improving software development and testing over the course of several decades. Recent research in fuzzing has focused on applications of machine learning (ML), offering useful tools to overcome challenges in the fuzzing process. This review surveys the current research in applying ML to fuzzing. Specifically, this review discusses successful applications of ML to fuzzing, briefly explores challenges encountered, and motivates future research to address fuzzing bottlenecks.
Forward citations
Cited by 2 Pith papers
-
SoK: Where to Fuzz? Assessing Target Selection Methods in Directed Fuzzing
Simple code metrics, especially Leopard's vulnerability scores, retrieve crash-relevant functions more accurately than sanitizer heuristics, recently-changed code, or deep learning models on a corpus of 1,621 real crashes.
-
Empirical Notes on the Interaction Between Continuous Kernel Fuzzing and Development
A descriptive study of syzbot-reported kernel crashes finds BSD kernels fix fuzz-found bugs faster, about 23 percent of Linux fixes are reviewed or tested, and only files-modified code churn weakly predicts Linux fix times.
Discussion (0). Continue with ORCID to comment.