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.
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|code2vec: Learning Distributed Representations of Code|
Uri AlonTechnion, Meital ZilbersteinTechnion, Omer LevyUniversity of Washington, USA, Eran YahavTechnionLink to publication DOI Pre-print Media Attached File Attached
|An Abstract Domain for Certifying Neural Networks|
Research PapersLink to publication DOI Media Attached
|Closed Forms for Numerical Loops|
Zachary KincaidPrinceton University, Jason BreckUniversity of Wisconsin - Madison, John CyphertUniversity of Wisconsin - Madison, Thomas RepsUniversity of Wisconsin - Madison and GrammaTech, Inc.Link to publication DOI Media Attached File Attached