\noindent {\em BACKGROUND:} Despite decades of research,
there is no agreement on what is  the ``best'' software effort estimator.

\noindent {\em AIM:} We seek stable conclusions about the ``best'' effort
estimator.  

\noindent {\em METHOD:} 10 learners and 9 data preprocessors, combined
into 90 effort estimators, were applied to twenty datasets.
Performance was assessed using
MAR, MMRE, MMER, MBRE, MIBRE, MdMRE, PRED(25) and compared using a
Wilcoxon test (95\%).  

\noindent {\em RESULTS:} We have abundance of algorithms to distinguish
``best'' estimators. However, number of publicly available datasets is limited 
to propose \textit{strong/weak} datasets.

\noindent {\em CONCLUSIONS:} 
We recommend regression trees or analogy methods (and  recommend
against neural nets or simple linear regression).  We
caution that prior work has not taken proper account of the data
used to evaluate the effort estimator. Future work should take
greater care to explore the data and its preprocessing (and not just the learner).
Furthermore, future work
should focus on providing more data to be able to distinguish {\em stronger} datasets.


