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

  • Mou L
  • Men R
  • Li G
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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.

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  • Lili Mou

  • Rui Men

  • Ge Li

  • Lu Zhang

  • Zhi Jin

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