A Review of Machine Learning Applications in Fuzzing
It addresses the problem of improving fuzzing efficiency for software developers and testers, but it is incremental as it reviews existing work without new results.
This review surveys how machine learning is applied to fuzzing to overcome challenges in software testing, summarizing successful applications and identifying future research directions.
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.