Structured Generative Models of Natural Source Code

C.J. Maddison, D. Tarlow. ICML 2014

We study the problem of building generative models of natural source code (NSC); that is, source code written by humans and meant to be understood by humans. Our primary con- tribution is to describe new generative models that are tailored to NSC. The models are based on probabilistic context free grammars (PCFGs) and neuro-probabilistic language models (Mnih & Teh, 2012), which are extended to incorporate additional source code-specific structure. These models can be efficiently trained on a corpus of source code and outperform a variety of less structured baselines in terms of predictive log likelihoods on held-out data.