\begin{abstract}
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\textbf{\textit{Background}}:
Despite decades of research,
there is no census on which
software effort estimation models are most useful.

\textit{\textbf{Aim:}} 
Prior work has reported that,
given $M$ estimation methods, no single model consistently outperforms all others.
Perhaps  rather than  recommend {\em one} estimation method as {\em best}, it is wiser
to generate estimates from
{\em ensembles of multiple estimation models}.

\textit{\textbf{Method:}} 9 learning algorithms were combined
with 10 pre-processors to generate $9 \times 10 = 90$ {\em
solo-methods}. These were applied to 20 data sets and evaluated
using 7 error measures.  This identified the best $n$ (in our case $n=13$)
solo-methods that exhibited stable performance
across multiple datasets \& error measures.
The top 2, 4, 8 and 13 solo-methods were
then combined to generate
 12 {\em multi-methods}, which were then   compared to the
solo-methods (using Wilcoxon test, 95\% confidence).

\textit{\textbf{Results:}} (i) The top 9 (out of 12) multi-methods
significantly out-performed \textit{all} 90 solo-models.   (ii)
The error rates of the multi-methods were significantly less than the solo-methods.
(iii)
The ranking of the
best  multi-method was remarkably stable.

\textit{\textbf{Conclusion:}} While there is no best single effort
estimation method, there exist best combinations of such effort
estimation methods.

\end{abstract}
