  We introduce the notion of property signatures, a representation for programs
and program specifications meant for consumption by machine learning
algorithms. Given a function with input type $\tau_{in}$ and output type
$\tau_{out}$, a property is a function of type: $(\tau_{in}, \tau_{out})
\rightarrow \texttt{Bool}$ that (informally) describes some simple property of
the function under consideration. For instance, if $\tau_{in}$ and $\tau_{out}$
are both lists of the same type, one property might ask `is the input list the
same length as the output list?'. If we have a list of such properties, we can
evaluate them all for our function to get a list of outputs that we will call
the property signature. Crucially, we can `guess' the property signature for a
function given only a set of input/output pairs meant to specify that function.
We discuss several potential applications of property signatures and show
experimentally that they can be used to improve over a baseline synthesizer so
that it emits twice as many programs in less than one-tenth of the time.
