Collecting the data required for quality prediction is a time-consuming and expensive process. 
It is therefore tempting 
to make quality predictions using public domain data,
collected from elsewhere. 

The literature is contradictory on the value of
effort estimates based on such cross-company data.
We hypothesis that "data irrelevancies" is the root cause 
of unreliable cross-company effort estimation. To test that,
this study 
takes
three groupings of three projects, and trains
effort estimators for project X using either "local data"
(from 
the same project)
or "imported data" (from other projects). 
This 
is repeated with and without relevancy filtering. 

Our results show that without relevancy filtering, 
imported data performed significantly worse than 
using local data. However, with relevancy filtering, 
imported data performed no worse 
than local data. That is,
reliable estimates _can_ be generated from cross-company data, provided a relevancy filter is applied 
prior to prediction. 

