Web: http://arxiv.org/abs/2201.08441

Jan. 24, 2022, 2:10 a.m. | Laura Wartschinski, Yannic Noller, Thomas Vogel, Timo Kehrer, Lars Grunske

cs.LG updates on arXiv.org arxiv.org

Context: Identifying potential vulnerable code is important to improve the
security of our software systems. However, the manual detection of software
vulnerabilities requires expert knowledge and is time-consuming, and must be
supported by automated techniques. Objective: Such automated vulnerability
detection techniques should achieve a high accuracy, point developers directly
to the vulnerable code fragments, scale to real-world software, generalize
across the boundaries of a specific software project, and require no or only
moderate setup or configuration effort. Method: In this …

arxiv deep deep learning detection learning natural python

More from arxiv.org / cs.LG updates on arXiv.org

Director, Data Engineering and Architecture

@ Chainalysis | California | New York | Washington DC | Remote - USA

Deep Learning Researcher

@ Topaz Labs | Dallas, TX

Sr Data Engineer (Contractor)

@ SADA | US - West

Senior Cloud Database Administrator

@ Findhelp | Remote

Senior Data Analyst

@ System1 | Remote

Speech Machine Learning Research Engineer

@ Samsung Research America | Mountain View, CA