A deep language model for software code

@article{Dam2016ADL,
  title={A deep language model for software code},
  author={Khanh Hoa Dam and Truyen Tran and Trang Pham},
  journal={CoRR},
  year={2016},
  volume={abs/1608.02715}
}
Existing language models such as n-grams for software code often fail to capture a long context where dependent code elements scatter far apart. In this paper, we propose a novel approach to build a language model for software code to address this particular issue. Our language model, partly inspired by human memory, is built upon the powerful deep learning-based Long Short Term Memory architecture that is capable of learning long-term dependencies which occur frequently in software code… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 12 references

Toward Deep Learning Software Repositories

2015 IEEE/ACM 12th Working Conference on Mining Software Repositories • 2015
View 4 Excerpts
Highly Influenced

Recurrent neural network language model training with noise contrastive estimation for speech recognition

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) • 2015
View 1 Excerpt

Advances in optimizing recurrent networks

2013 IEEE International Conference on Acoustics, Speech and Signal Processing • 2013
View 1 Excerpt

On the naturalness of software

2012 34th International Conference on Software Engineering (ICSE) • 2012
View 2 Excerpts

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