Code2vec: Learning distributed representations of code

Uri Alon, Meital Zilberstein, Omer Levy, Eran Yahav

Research output: Contribution to journalArticlepeer-review

Abstract

We present a neural model for representing snippets of code as continuous distributed vectors (łcode embed-dingsž). The main idea is to represent a code snippet as a single fixed-length code vector, which can be used to predict semantic properties of the snippet. To this end, code is first decomposed to a collection of paths in its abstract syntax tree. Then, the network learns the atomic representation of each path while simultaneously learning how to aggregate a set of them. We demonstrate the effectiveness of our approach by using it to predict a method's name from the vector representation of its body. We evaluate our approach by training a model on a dataset of 12M methods. We show that code vectors trained on this dataset can predict method names from files that were unobserved during training. Furthermore, we show that our model learns useful method name vectors that capture semantic similarities, combinations, and analogies. A comparison of our approach to previous techniques over the same dataset shows an improvement of more than 75%, making it the first to successfully predict method names based on a large, cross-project corpus. Our trained model, visualizations and vector similarities are available as an interactive online demo at http://code2vec.org. The code, data and trained models are available at https://github.com/tech-srl/code2vec.

Original languageEnglish
Article number40
JournalProceedings of the ACM on Programming Languages
Volume3
Issue numberPOPL
DOIs
StatePublished - Jan 2019

Keywords

  • Big Code
  • Distributed Representations
  • Machine Learning

All Science Journal Classification (ASJC) codes

  • Software
  • Safety, Risk, Reliability and Quality

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