  Programming-by-Example (PBE) systems synthesize an intended program in some
(relatively constrained) domain-specific language from a small number of
input-output examples provided by the user. In this paper, we motivate and
define the problem of quantitative PBE (qPBE) that relates to synthesizing an
intended program over an underlying (real world) programming language that also
minimizes a given quantitative cost function. We present a modular approach for
solving qPBE that consists of three phases: intent disambiguation, global
search, and local search. On two concrete objectives, namely program
performance and size, our qPBE procedure achieves $1.53 X$ and $1.26 X$
improvement respectively over the baseline FlashFill PBE system, averaged over
$701$ benchmarks. Our detailed experiments validate the design of our procedure
and show the value of combining global and local search for qPBE.
