  This paper envisions an end-to-end program generation scenario using
recurrent neural networks (RNNs): Users can express their intention in natural
language; an RNN then automatically generates corresponding code in a
characterby-by-character fashion. We demonstrate its feasibility through a case
study and empirical analysis. To fully make such technique useful in practice,
we also point out several cross-disciplinary challenges, including modeling
user intention, providing datasets, improving model architectures, etc.
Although much long-term research shall be addressed in this new field, we
believe end-to-end program generation would become a reality in future decades,
and we are looking forward to its practice.
