  Performance becomes an issue particularly when execution cost hinders the
functionality of a program. Typically a profiler can be used to find program
code execution which represents a large portion of the overall execution cost
of a program. Pinpointing where a performance issue exists provides a starting
point for tracing cause back through a program.
  While profiling shows where a performance issue manifests, we use mutation
analysis to show where a performance improvement is likely to exist. We find
that mutation analysis can indicate locations within a program which are highly
impactful to the overall execution cost of a program yet are executed
relatively infrequently. By better locating potential performance improvements
in programs we hope to make performance improvement more amenable to
automation.
