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\begin{document}

\title{On the Relative Value of Cross-Company and Within-Company Data
for Defect Prediction}
%\subtitle{Do you have a subtitle?\\ If so, write it here}

%\titlerunning{Short form of title}        % if too long for running head

\author{Burak Turhan         \and
        Tim Menzies \and
        Ayse Bener \and
        Justin Distefano%etc.
}

%\authorrunning{Short form of author list} % if too long for running head

\institute{Burak Turhan \at
              Department of Computer Engineering, Bogazici University, Istanbul, Turkey \\
%              Tel.: +90-212-3597227\\
  %            Fax: +90-212-287-2461\\
              \email{turhanb@boun.edu.tr}           %  \\
%             \emph{Present address:} of F. Author  %  if needed
           \and
           Tim Menzies \at
              Lane Department of Computer Science and Electrical Engineering, West Virginia\\
              \email{tim@menzies.us} 
           \and
              Ayse Bener \at
              Department of Computer Engineering, Bogazici University, Istanbul, Turkey \\
%              Tel.: +90-212-3597226\\
%              Fax: +90-212-287-2461\\
              \email{bener@boun.edu.tr} 
            \and
           Justin Distefano \at
              Lane Department of Computer Science and Electrical Engineering, West Virginia\\
              \email{jdistefano@ismwv.com} }

\date{Received: date / Accepted: date}
% The correct dates will be entered by the editor



%\author{Burak~Turhan,~\IEEEmembership{Student~Member,~IEEE,}
%        Tim~Menzies,~\IEEEmembership{Member,~IEEE,}
%        Ayse~Bener,~\IEEEmembership{Member,~IEEE,}\\ 
%		and Justin~Distefano
%\thanks{Mr. Turhan and Dr. Bener are with the 
%Department of Computer Engineering, Bogazici University, Turkey.
%Emails:
%\protect\url{turhanb@boun.edu.tr},
%\protect\url{bener@boun.edu.tr}.
%}
%\thanks{Dr. Menzies and  Mr. Distefano
%are with the Lane Department of Computer Science and Electrical Engineering, 
%West Virginia
%University.
%Mr. Distefano is also Chief Programmer at Integrated Software Metrics.
%Emails: \protect\url{tim@menzies.us}
%and \protect\url{jdistefano@ismwv.com}
%}
%\thanks{The research described in this paper
%was supported by
%Bogazici University research fund under grant number BAP-06HA104, by TUBITAK under grant number EEEAG 108E014
%and at
%West Virginia  University
%under grants with NASA's Software Assurance Research Program.
%Reference herein to any specific commercial product, process, or service
%by trademark, manufacturer, or otherwise, does not constitute or imply
%its endorsement by the United States Government.
%}\thanks{See 
%See \protect\url{http://menzies.us/pdf/07interra.pdf} for an earlier draft of this paper.
%Manuscript received June, 2008; revised XXX,}} 

%\markboth{Journal of ???,~Vol.~6, No.~1, January~2008}%
%{Turhan \MakeLowercase{\textit{et al.}}: On the Comparative Value of Local Data for Defect Prediction}

%We found that the cross-company model provided poor 
%predictions for the single-company projects and much worse 
%predictions than the single-company model.
\maketitle

\begin{abstract}

We propose a practical defect prediction approach for companies that do not track 
defect related data. Specifically, we investigate the applicability of cross-company (CC) data for building localized defect
predictors using static code features. 

Firstly, we analyze the conditions, where
CC data can be used as is. These conditions turn out to be quite few. Then we apply principles of analogy-based learning (i.e. nearest neighbor (NN) filtering) to CC data, in order to fine tune these models for localization. We compare the performance of these models with that of defect predictors learned from within-company (WC) data. As expected, we observe that defect predictors learned from WC data outperform the ones learned from CC data. However, our analyses also yield defect predictors learned from NN-filtered CC data, with performance close
to, but still not better than, WC data.  
Therefore, we perform a final analysis for determining the minimum number of local defect reports in order to learn WC defect predictors. We demonstrate in this paper that the minimum number of data
samples required to build effective defect predictors can be quite
small and can be collected quickly within a few months.


Hence, for companies with no local defect data, we recommend a two-phase approach that allows them to employ the defect prediction process instantaneously. 
In phase one,
companies should use NN-filtered CC data to initiate the defect
prediction process and simultaneously start collecting WC (local)
data. Once enough WC data is collected (i.e. after a few months), organizations
should switch to phase two and use predictors learned from WC data.

KEYWORDS: defect prediction; learning; metrics (product metrics); cross-company; within-company; nearest-neighbor filtering
\end{abstract}

\end{document}