Learning to Repair Software Vulnerabilities with Generative Adversarial Networks
| 
41 | 
36 | 
0
Abstract
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 domain without requiring paired labeled examples or source and target domains to be bijections. We demonstrate that the proposed adversarial learning approach is an effective technique for repairing software vulnerabilities, performing close to seq2seq approaches that require labeled pairs. The proposed Generative Adversarial Network approach is application-agnostic in that it can be applied to other problems similar to code repair, such as grammar correction or sentiment translation.
还没有评论，说两句吧！
热门资源
Learning to learn...
The move from hand-designed features to learned...
A Mathematical Mo...
Direct democracy, where each voter casts one vo...
Stratified Strate...
In this paper we introduce Stratified Strategy ...
Rating-Boosted La...
The performance of a recommendation system reli...
Hierarchical Task...
We extend hierarchical task network planning wi...
智能在线
400-630-6780
聆听.建议反馈
E-mail: support@tusaishared.com
资源分类
