We introduce a new generative model for code called probabilistic higher order grammar (PHOG). PHOG generalizes probabilistic context free grammars (PCFGs) by allowing conditioning of a production rule beyond the parent non-terminal, thus capturing rich contexts relevant to programs. Even though PHOG is more powerful than a PCFG, it can be learned from data just as efficiently. We trained a PHOG model on a large JavaScript code corpus and show that it is more precise than existing models, while similarly fast. As a result, PHOG can immediately benefit existing programming tools based on probabilistic models of code.

@inproceedings{bielik2016phog, title={PHOG: probabilistic model for code}, author={Bielik, Pavol and Raychev, Veselin and Vechev, Martin}, booktitle={International Conference on Machine Learning}, pages={2933--2942}, year={2016}}