  Detecting security vulnerabilities in software before they are exploited has
been a challenging problem for decades. Traditional code analysis methods have
been proposed, but are often ineffective and inefficient. In this work, we
model software vulnerability detection as a natural language processing (NLP)
problem with source code treated as texts, and address the automated software
venerability detection with recent advanced deep learning NLP models assisted
by transfer learning on written English. For training and testing, we have
preprocessed the NIST NVD/SARD databases and built a dataset of over 100,000
files in $C$ programming language with 123 types of vulnerabilities. The
extensive experiments generate the best performance of over 93\% accuracy in
detecting security vulnerabilities.
