Predicting Program Properties from "Big Code"

8 years 6 months ago

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We present a new approach for predicting program properties from massive codebases (aka “Big Code”). Our approach ﬁrst learns a probabilistic model from existing data and then uses this model to predict properties of new, unseen programs. The key idea of our work is to transform the input program into a representation which allows us to phrase the problem of inferring program properties as structured prediction in machine learning. This formulation enables us to leverage powerful probabilistic graphical models such as conditional random ﬁelds (CRFs) in order to perform joint prediction of program properties. As an example of our approach, we built a scalable prediction engine called JSNICE 1 for solving two kinds of problems in the context of JavaScript: predicting (syntactic) names of identiﬁers and predicting (semantic) type annotations of variables. Experimentally, JSNICE predicts correct names for 63% of name identiﬁers and its type annotation predictions are correct i...

Veselin Raychev, Martin T. Vechev, Andreas Krause

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