{"paper":{"title":"code2vec: Learning Distributed Representations of Code","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.PL","stat.ML"],"primary_cat":"cs.LG","authors_text":"Eran Yahav, Meital Zilberstein, Omer Levy, Uri Alon","submitted_at":"2018-03-26T09:05:30Z","abstract_excerpt":"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 $\\textit{code vector}$, 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 $\\textit{simultaneously}$ 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 repres"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.09473","kind":"arxiv","version":5},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}