A Machine Learning Framework for Programming by Example
Learning programs is a timely and interesting
challenge. In Programming by Example (PBE),
a system attempts to infer a program from input
and output examples alone, by searching for
a composition of some set of base functions.
We show how machine learning can be used to
speed up this seemingly hopeless search problem,
by learning weights that relate textual features
describing the provided input-output examples
to plausible sub-components of a program.
This generic learning framework lets us
address problems beyond the scope of earlier
PBE systems. Experiments on a prototype implementation
show that learning improves search
and ranking on a variety of text processing tasks
found on help forums.