\begin{kasten}
    \section*{ \hspace{0.1cm} {\color{red} \underline{ABSTRACT}}}
    \Large{
\hspace{6mm}Collecting the data required for quality prediction {\em within}
a development team is time-consuming
and expensive.  An alternative is to make 
predictions using data that {\em crosses}  from other projects
or even other companies.
We show that with/without {\em relevancy filtering}, imported data
performs the same/worse (respectively)  than using local data. 
Therefore, we recommend the use of relevancy filtering whenever
generating estimates using data from another project.
    }
\end{kasten}

\begin{kasten}
  \section*{ \hspace{0.1cm} {\color{red} \underline{INTRODUCTION}}}
\large{
When  data is scarce {\em within} one project, it is tempting to use
data imported from other projects.  Such
{\em cross}-project data exist; for example the PROMISE repository
\cite{Boetticher2007} offers a dozen effort estimation data sets
for public access.

A  recent survey
paper has evaluated {\em within} or {\em cross} data for effort
estimation~\cite{Kitchenham2007}. They concluded that they
could not make a conclusion; that the current findings are contradictory
about the relative merits of {\em within} or {\em cross} data.

In other work~\cite{Turhan2009}, we have shown that it is
acceptable to
use {\em cross} data sources for defect prediction, providing that data has been  pre-processed by some
sort of {\em relevancy filtering}.
Given a large training set, such relevancy filters select a small subset relevant to the current test case.
Such filtering removes training instances that create noise in the estimation
process, leaving a body of data that, in theory, follows the principle
of locality.

The success of relevancy filtering for defect prediction prompts us to
apply it to effort estimation. 
To the best of our knowledge, this is
the first exploration in the effort estimation community of the
effects of relevancy filtering when applied to {\em cross} and {\em within}
project data.
We show that
{\em cross} data
can usually attain estimation accuracies just as high as those of {\em within} data, provided that a relevancy filter
is applied to the data, prior to making estimates.
}
\end{kasten}

\begin{kasten}
  \section*{ \hspace{0.1cm} {\color{red} \underline{ RELEVANCY FILTERING}}}
  \large{Our relevancy filter  extends standard analogy-based estimation methods (which we call ABE0).
ABE0
generates estimates for a test project by gathering evidence from the
effort values of similar projects in some training set.
By analyzing the previous research of experts like Shepperd et. al.\cite{Shepperd1996}, 
Mendes et. al.\cite{Mendes2003} and Li et. al.\cite{Li2009} on the field of analogy-based estimation, we can come up with a baseline technique.
The baseline technique works as follows:
\bi
\item Build a training data from rows of past projects;
\item The columns of this set are composed of \textit{independent} variables (the features that define projects) and a \textit{dependent} 
variable (the recorded effort value). 
\item Decide on how many similar projects (\textit{analogies}) to use when examining a new test instance
, i.e. \textit{k}-values.
\ei
  }
\end{kasten}
