Reviews: Learning to Repair Software Vulnerabilities with Generative Adversarial Networks

Update based on author rebuttal: The authors address some of my criticisms and promise to improve some of the motivation in subsequent drafts. This paper proposes a system for correcting sequences, with a target application of fixing buggy source code. They use a sequence-to-sequence model within a GAN framework, which allows the model to be trained without paired source/target data. Some additional new tricks are proposed to make the model output consistent translations of the input. The model is tested on two synthetic tasks and a source code correction benchmark.