NDLI: Predicting Program Properties from "Big Code"

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Predicting Program Properties from "Big Code"

Content Provider	ACM Digital Library
Author	Vechev, Martin Raychev, Veselin Krause, Andreas
Abstract	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 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 in 81% of the cases. In the first week since its release, JSNice was used by more than 30,000 developers and in only few months has become a popular tool in the JavaScript developer community. By formulating the problem of inferring program properties as structured prediction and showing how to perform both learning and inference in this context, our work opens up new possibilities for attacking a wide range of difficult problems in the context of "Big Code" including invariant generation, decompilation, synthesis and others.
Starting Page	111
Ending Page	124
Page Count	14
File Format	PDF QT / MOV
ISBN	9781450333009
DOI	10.1145/2676726.2677009
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-01-14
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Names Types Closure compiler Javascript Conditional random fields Program properties Big code Structured prediction
Content Type	Video Text
Resource Type	Article

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