  Inductive programming frequently relies on some form of search in order to
identify candidate solutions. However, the size of the search space limits the
use of inductive programming to the production of relatively small programs. If
we could somehow correctly predict the subset of instructions required for a
given problem then inductive programming would be more tractable. We will show
that this can be achieved in a high percentage of cases. This paper presents a
novel model of programming language instruction co-occurrence that was built to
support search space partitioning in the Zoea distributed inductive programming
system. This consists of a collection of intersecting instruction subsets
derived from a large sample of open source code. Using the approach different
parts of the search space can be explored in parallel. The number of subsets
required does not grow linearly with the quantity of code used to produce them
and a manageable number of subsets is sufficient to cover a high percentage of
unseen code. This approach also significantly reduces the overall size of the
search space - often by many orders of magnitude.
