Blogs (1) >>
POPL 2019
Sun 13 - Sat 19 January 2019 Cascais, Portugal
Wed 16 Jan 2019 15:21 - 15:43 at Sala I - Machine Learning and Linear Algebra Chair(s): Aws Albarghouthi

We present a neural model for representing snippets of code as continuous distributed vectors (``code embeddings''). 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. This is performed by decomposing code to a collection of paths in its abstract syntax tree, and learning the atomic representation of each path simultaneously with 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 14M methods. We show that code vectors trained on this dataset can predict method names from files that were completely unobserved during training. Furthermore, we show that our model learns useful method name vectors that capture semantic similarities, combinations, and analogies.

Comparing previous techniques over the same data set, our approach obtains a relative improvement of over 75%, being 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.

Wed 16 Jan

POPL-2019-Research-Papers
15:21 - 16:27: Research Papers - Machine Learning and Linear Algebra at Sala I
Chair(s): Aws AlbarghouthiUniversity of Wisconsin-Madison
POPL-2019-Research-Papers15:21 - 15:43
Talk
Uri AlonTechnion, Meital ZilbersteinTechnion, Omer LevyUniversity of Washington, USA, Eran YahavTechnion
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POPL-2019-Research-Papers15:43 - 16:05
Talk
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POPL-2019-Research-Papers16:05 - 16:27
Talk
Zachary KincaidPrinceton University, Jason BreckUniversity of Wisconsin - Madison, John CyphertUniversity of Wisconsin - Madison, Thomas RepsUniversity of Wisconsin - Madison and GrammaTech, Inc.
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