Learning to Repair Software Vulnerabilities with Generative Adversarial Networks
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.
Cite
Text
Harer et al. "Learning to Repair Software Vulnerabilities with Generative Adversarial Networks." Neural Information Processing Systems, 2018.Markdown
[Harer et al. "Learning to Repair Software Vulnerabilities with Generative Adversarial Networks." Neural Information Processing Systems, 2018.](https://mlanthology.org/neurips/2018/harer2018neurips-learning/)BibTeX
@inproceedings{harer2018neurips-learning,
title = {{Learning to Repair Software Vulnerabilities with Generative Adversarial Networks}},
author = {Harer, Jacob and Ozdemir, Onur and Lazovich, Tomo and Reale, Christopher and Russell, Rebecca and Kim, Louis and Chin, Peter},
booktitle = {Neural Information Processing Systems},
year = {2018},
pages = {7933-7943},
url = {https://mlanthology.org/neurips/2018/harer2018neurips-learning/}
}