\begin{abstract}
~\\\textit{Background}:
Prior work on effort estimation reported much variance in the rankings of different models. What seemed ``best'' according
to (say) mean magnitude of the relative error (MRE) was not ``best'' when according to another performance measure.
However, those results were based on simulation studies using distributions from one data set. Recently, numerous
data sets have become available.
\\\textit{Aim:} 
We seek to test if ranking variance persists in a larger range of data then previously studied.
\\\textit{Method:}
Nine learning algorithms were combined with eleven pre-processors to generate 99 methods which 
were then applied to 20 data sets. The methods was then assessed by compariong
their performance using a Wilcoxon (95\%) test using median/mean MRE; median/mean absolute residual;
median/mean balanced MRE; median/mean MER; and PRED(25). These experiments were conducted using
leave-one-out as well as 10*three-way cross-validation. 
\\\textit{Results:}
In contrast to prior results,
the same general pattern repeated for all performance measures and in both cross-validation treatments.
\\\textit{Conclusion:}
Given the amount data now available to SE researchers,
we are now able to rank various effort estimation models. Further, we can now rank different data sets
on their ability to distinquish between models.
\end{abstract}
