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Makale: On End-to-End Program Generation from User Intention by Deep Neural Networks

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 character 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 eld, we believe end-to-end program generation would become a reality in future decades, and we are looking for ward to its practice.