Learning to Repair Software Vulnerabilities with Generative Adversarial Networks

NeurIPS 2018 Jacob HarerOnur OzdemirTomo LazovichChristopher P. RealeRebecca L. RussellLouis Y. KimPeter Chin

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... (read more)

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