  Overlapping instruction subsets derived from human originated code have
previously been shown to dramatically shrink the inductive programming search
space, often by many orders of magnitude. Here we extend the instruction subset
approach to consider direct instruction-instruction applications (or
instruction digrams) as an additional search heuristic for inductive
programming. In this study we analyse the frequency distribution of instruction
digrams in a large sample of open source code. This indicates that the
instruction digram distribution is highly skewed with over 93% of possible
instruction digrams not represnted in the code sample. We demonstrate that
instruction digrams can be used to constrain instruction selection during
search, further reducing size of the the search space, in some cases by several
orders of magnitude. This significantly increases the size of programs that can
be generated using search based inductive programming techniques. We discuss
the results and provide some suggestions for further work.
