\section{Conclusion}
Our reading of the literature is that much prior assessment of reuse has focused
on a very narrow range of of issues. Here, we have tried broadening the debate by assessing reuse
with respect to the broader context of minimizing effort and defects and development time.

This paper has explored the case of (a) ranking reuse against different effort, schedule,
and defect reduction strategies using (b) models with competing influences
that (c) have not been precisely tuned using local data. In this case, we
avoided the need for local data to calibrate the models
 via using the STAR tool. STAR leaves the calibration variables of a model unconstrained,
then uses AI search to find project options that most reduces effort, development time, and defects.
 
STAR was applied here to a study of four incremental reuse strategies and the eight drastic 
changes. These 13 project changes were 
applied to two NASA case studies.  We found that reuse strategies
in general performed as well or better than drastic change strategies on
ground software, but did worse than adopting no strategy in the case of 
flight software systems.

These results suggest that project managers looking for implementing
software reuse into their projects may find worthwhile checking the relative
merits of reuse against other project improvement options. That is, the relative
merits of software reuse should be evaluated in a project-by-project basis.
reuse strategies against other project improvement strategies.


In conclusion, in theory, software reuse is  an attractive approach to any software project
capable of adopting it. However,  in practice, reuse might not be
the most useful strategy and changing something else  (or changing nothing at all) 
could be more beneficial. 
AI search over process simulation models is useful for finding the best changes,  particularly when
there is not sufficient data for precisely tuning a simulation model.
