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

Lili Mou; Rui Men; Ge Li; Lu Zhang; Zhi Jin

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arXiv · 1510.07211

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

Lili Mou·Rui Men·Ge Li·Lu Zhang·Zhi Jin

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Abstract

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. 2015-10-25. On End-to-End Program Generation from User Intention by Deep Neural Networks. https://arxiv.org/abs/1510.07211

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