The main problem when automatically analysing source code is finding a meaningful representation that captures the specific properties of programming languages. While there are widely adopted techniques for building “word embeddings” for natural language (like Word2vec), these are not applicable to source code, whose syntax and vocabulary are simpler .
This paper presents a model for learning “code embeddings”, relying on the syntactic structure of the code, expressed as Abstract Syntax Trees, and how to use those embeddings for code classification tasks.
Paper is available here: http://arxiv.org/abs/1409.3358
Talk will be in english
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Papers We Love Madrid
Madrid chapter of Papers We Love What was the last paper within the realm of computing you read? What did it inspire you to build or tinker with? Come share the ideas in an awesome academic/research publication with fellow engineers, programmers, and paper-readers. Lead a session and show off code that you wrote that implements these ideas or just give us the lowdown about the paper. Otherwise, just come, listen, learn, and discuss. We'll be using papers-we-love's curated repository . Please contribute by
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