Neural Programming by Example

Authors

  • Chengxun Shu Beihang University
  • Hongyu Zhang The University of Newcastle

DOI:

https://doi.org/10.1609/aaai.v31i1.10734

Keywords:

Programming by Example, Deep Learning, Program Induction

Abstract

Programming by Example (PBE) targets at automatically inferring a computer program for accomplishing a certain task from sample input and output. In this paper, we propose a deep neural networks (DNN) based PBE model called Neural Programming by Example (NPBE), which can learn from input-output strings and induce programs that solve the string manipulation problems. Our NPBE model has four neural network based components: a string encoder, an input-output analyzer, a program generator, and a symbol selector. We demonstrate the effectiveness of NPBE by training it end-to-end to solve some common string manipulation problems in spreadsheet systems. The results show that our model can induce string manipulation programs effectively. Our work is one step towards teaching DNN to generate computer programs.

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Published

2017-02-12

How to Cite

Shu, C., & Zhang, H. (2017). Neural Programming by Example. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.10734

Issue

Section

Main Track: Machine Learning Applications