Bayesian Sketch Learning for Program Synthesis

We present a data-driven approach to the problem of inductive computer program synthesis. Our method learns a probabilistic model for real-world programs from a corpus of existing code. It uses this model during synthesis to automatically infer a posterior distribution over sketches, or syntactic models of the problem to be synthesized. Sketches sampled from this posterior are then used to drive combinatorial synthesis of a program in a high-level programming language. The key technical innovation of our approach --- embodied in a system called Bayou --- is utilizing user-supplied evidence as to the program's desired behavior, along with a Bayesian update, to obtain a posterior distribution over the program's true, latent specification (indicating user intent), which in turn produces a posterior over possible sketches. As we show experimentally, explicitly modeling uncertainty in specification significantly increases the accuracy of the synthesis algorithm. We evaluate Bayou's ability to synthesize Java and Android methods. We find that using just a few example API sequences to communicate user intent, Bayou can synthesize complex method bodies, some implementing tasks never encountered during training.