Title: Neural Sketch Learning for Conditional Program Generation. Abstract: We study the problem of generating source code in a strongly typed,
Java-like programming language, given a label (for example a set of
API calls or types) carrying a small amount of information about the
code that is desired. The generated programs are expected to respect a
`"realistic" relationship between programs and labels, as exemplified
by a corpus of labeled programs available during training.

Two challenges in such *conditional program generation* are that
the generated programs must satisfy a rich set of syntactic and
semantic constraints, and that source code contains many low-level
features that impede learning.  We address these problems by training
a neural generator not on code but on *program sketches*, or
models of program syntax that abstract out names and operations that
do not generalize across programs. During generation, we infer a
posterior distribution over sketches, then concretize samples from
this distribution into type-safe programs using combinatorial
techniques.  We implement our ideas in a system for generating
API-heavy Java code, and show that it can often predict the entire
body of a method given just a few API calls or data types that appear
in the method.