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.
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, de-compilation, synthesis and others.