Structured Generative Models of Natural Source Code
Chris J. Maddison, Daniel 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.
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