  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.
