\begin{figure}
\begin{center}
\begin{tabular}{r|rr}
study & NOVA & {\W}\\\hline
nasa93 flight & 72\% & 61\% \\ 
nasa93 ground & 73\% & 77\%\\
nasa93 osp & 42\%    & 49\% \\
nasa93 ops2 & 5\%   & 27\%\\
\end{tabular}
\end{center}
\caption{
Comparing  reductions ($\frac{initial - final}{initial}$) reported
by NOVA (from~\cite{me09i}) and {\W} (from this report).
}\label{fig:compare}
\end{figure}

\section{Discussion}

Our recent work has two types of  solutions to the problem
of finding changes that most effect a project.
Both methods search for changes that most effect (and
improve) the output effort
estimates.
\bi
\item {\W} is a {\em case-based} method  that searches
relevant
historical examples.
\item NOVA/STAR~\cite{me09i}  is a  {\em model-based} methods that
applies AI search to the input space of software process models.
\ei
\fig{compare} shows that estimation reductions reported by {\W} (in
this report) to the reductions reported previously~\cite{me09i}.
This table is much shorter than \fig{tsize} since {\W} can
be applied to a wider range of data sets than NOVA/STAR.

It is enouraging to note that the results in \fig{compare}
are similar (and {\W} reductions are not always less than those
found by NOVA/STAR 
However, 
the numbers in \fig{compare} cannot be directly compared due to the different
experimental methods that generated them (we are  currently building
a rig that will allow a rigorus statistical
 comparison of {\W} vs NOVA/STAR). 
Regardless of the results of that statistical analysis, there are 
clear indicators of when we should/ should not use {\W} or STAR/NOVA:
\bi
\item {\W} is a case-based method. If historical cases are not
available, then NOVA/STAR is the preferred method.
\item NOVA/STAR is based on the USC COCOMO suite of models.
If the local business users do not endorse that mode,
then {\W} is the preferred method.
\ei
