Deep learning based vulnerability detection: Are we there yet?
Automated detection of software vulnerabilities is a fundamental problem in software
security. Existing program analysis techniques either suffer from high false positives or false …
security. Existing program analysis techniques either suffer from high false positives or false …
Software vulnerability detection using deep neural networks: a survey
The constantly increasing number of disclosed security vulnerabilities have become an
important concern in the software industry and in the field of cybersecurity, suggesting that …
important concern in the software industry and in the field of cybersecurity, suggesting that …
Diversevul: A new vulnerable source code dataset for deep learning based vulnerability detection
We propose and release a new vulnerable source code dataset. We curate the dataset by
crawling security issue websites, extracting vulnerability-fixing commits and source codes …
crawling security issue websites, extracting vulnerability-fixing commits and source codes …
Software vulnerability analysis and discovery using deep learning techniques: A survey
Exploitable vulnerabilities in software have attracted tremendous attention in recent years
because of their potentially high severity impact on computer security and information safety …
because of their potentially high severity impact on computer security and information safety …
Neural transfer learning for repairing security vulnerabilities in c code
Z Chen, S Kommrusch… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In this paper, we address the problem of automatic repair of software vulnerabilities with
deep learning. The major problem with data-driven vulnerability repair is that the few …
deep learning. The major problem with data-driven vulnerability repair is that the few …
'Think secure from the beginning' A Survey with Software Developers
Vulnerabilities persist despite existing software security initiatives and best practices. This
paper focuses on the human factors of software security, including human behaviour and …
paper focuses on the human factors of software security, including human behaviour and …
On the capability of static code analysis to detect security vulnerabilities
K Goseva-Popstojanova, A Perhinschi - Information and Software …, 2015 - Elsevier
Context: Static analysis of source code is a scalable method for discovery of software faults
and security vulnerabilities. Techniques for static code analysis have matured in the last …
and security vulnerabilities. Techniques for static code analysis have matured in the last …
SCL-CVD: Supervised contrastive learning for code vulnerability detection via GraphCodeBERT
Detecting vulnerabilities in source code is crucial for protecting software systems from
cyberattacks. Pre-trained language models such as CodeBERT and GraphCodeBERT have …
cyberattacks. Pre-trained language models such as CodeBERT and GraphCodeBERT have …
Learning to repair software vulnerabilities with generative adversarial networks
Motivated by the problem of automated repair of software vulnerabilities, we propose an
adversarial learning approach that maps from one discrete source domain to another target …
adversarial learning approach that maps from one discrete source domain to another target …
Open science in software engineering: A study on deep learning-based vulnerability detection
Open science is a practice that makes scientific research publicly accessible to anyone,
hence is highly beneficial. Given the benefits, the software engineering (SE) community has …
hence is highly beneficial. Given the benefits, the software engineering (SE) community has …