Deep learning type inference

@article{Hellendoorn2018DeepLT,
  title={Deep learning type inference},
  author={V. Hellendoorn and C. Bird and Earl T. Barr and Miltiadis Allamanis},
  journal={Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering},
  year={2018}
}
  • V. Hellendoorn, C. Bird, +1 author Miltiadis Allamanis
  • Published 2018
  • Computer Science
  • Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering
Dynamically typed languages such as JavaScript and Python are increasingly popular, yet static typing has not been totally eclipsed: Python now supports type annotations and languages like TypeScript offer a middle-ground for JavaScript: a strict superset of JavaScript, to which it transpiles, coupled with a type system that permits partially typed programs. [...] Key Method We propose DeepTyper, a deep learning model that understands which types naturally occur in certain contexts and relations and can provide…Expand
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References

SHOWING 1-3 OF 3 REFERENCES
Predicting Program Properties from "Big Code"
  • 273
  • Highly Influential
  • PDF
Recovering clear, natural identifiers from obfuscated JS names
  • 40
  • Highly Influential
  • PDF
NeuroNER: an easy-to-use program for named-entity recognition based on neural networks
  • 112
  • Highly Influential
  • PDF