  The abundance of open-source code, coupled with the success of recent
advances in deep learning for natural language processing, has given rise to a
promising new application of machine learning to source code. In this work, we
explore the use of a Siamese recurrent neural network model on Python source
code to create vectors which capture the semantics of code. We evaluate the
quality of embeddings by identifying which problem from a programming
competition the code solves. Our model significantly outperforms a
bag-of-tokens embedding, providing promising results for improving code
embeddings that can be used in future software engineering tasks.
