  Deep learning has made significant breakthroughs in various fields of
artificial intelligence. Advantages of deep learning include the ability to
capture highly complicated features, weak involvement of human engineering,
etc. However, it is still virtually impossible to use deep learning to analyze
programs since deep architectures cannot be trained effectively with pure back
propagation. In this pioneering paper, we propose the "coding criterion" to
build program vector representations, which are the premise of deep learning
for program analysis. Our representation learning approach directly makes deep
learning a reality in this new field. We evaluate the learned vector
representations both qualitatively and quantitatively. We conclude, based on
the experiments, the coding criterion is successful in building program
representations. To evaluate whether deep learning is beneficial for program
analysis, we feed the representations to deep neural networks, and achieve
higher accuracy in the program classification task than "shallow" methods, such
as logistic regression and the support vector machine. This result confirms the
feasibility of deep learning to analyze programs. It also gives primary
evidence of its success in this new field. We believe deep learning will become
an outstanding technique for program analysis in the near future.
