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.
We give a method for generating type-safe programs in a Java-like language, given a small amount of syntactic information about the desired code.