  Recently there have been many advances in research on language modeling of
source code. Applications range from code suggestion and completion to code
summarization. However, complete program synthesis of industry-grade
programming languages remains an open problem. In this work, we introduce and
experimentally validate a variational autoencoder model for program synthesis
of industry-grade programming languages. This model makes use of the inherent
tree structure of code and can be used in conjunction with gradient free
optimization techniques like evolutionary methods to generate programs that
maximize a given fitness function, for instance, passing a set of test cases. A
demonstration is avaliable at https://tree2tree.app
