\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
AR, MRE, MER, MdMRE, MMRE, PRED(25), MIBRE and compared using a
Wilcoxon test (95\%).  

\noindent {\em RESULTS:} Some datasets are
{\em stronger/weaker} at distinguishing estimation performance and, hence,
are better/worse at finding the ``best''
estimators.  

\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, that future work
should  justify its conclusions using the {\em stronger} datasets.


