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SATToSE2015.tex
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\documentclass[a4paper]{article}
\usepackage{graphicx}
% \usepackage{twocolpceurws}
\usepackage{onecolpceurws}
\usepackage{float}
% \usepackage{floatrow}
\usepackage{caption}
% \usepackage{adjustbox}
\usepackage{subcaption}
\usepackage{subfig}
% \DeclareCaptionLabelFormat{andtable}{#1~#2 \& \tablename~\thetable}
\title{Predicting Software Quality through Network Analysis}
\author{
Giulio Concas \\
% Department of Electrical and Electronic Engineering
% DIEE \footnote{\label{diee}Department of Electrical and Electronic Engineering} \\ University of Cagliari \\
% Piazza D'Armi, 09123 \\ Cagliari (Italy) \\
\and
Michele Marchesi \\
% Department of Electrical and Electronic Engineering
% DIEE \footnote{\label{diee}Department of Electrical and Electronic Engineering} \\ University of Cagliari \\
% Piazza D'Armi, 09123 \\ Cagliari (Italy) \\
\and
Cristina Monni \\
% DIEE \\ University of Cagliari \\
% Piazza D'Armi, 09123 \\ Cagliari (Italy) \\
% Department of Electrical and Electronic Engineering \\ University of Cagliari \\
% Piazza D'Armi, 09123 Cagliari (Italy) \\
\and
Matteo Orr\'{u} \\
% DIEE \\ University of Cagliari \\
% Piazza D'Armi, 09123 \\ Cagliari (Italy) \\
% Department of Electrical and Electronic Engineering \\ University of Cagliari \\
% Piazza D'Armi, 09123 Cagliari (Italy) \\
\and
Roberto Tonelli \\
% DIEE \\ University of Cagliari \\
% Piazza D'Armi, 09123 \\ Cagliari (Italy) \\
% Department of Electrical and Electronic Engineering \\ University of Cagliari \\
% Piazza D'Armi, 09123 Cagliari (Italy) \\
}
\institution{Department of Electrical and Electronic Engineering (DIEE)\\
University of Cagliari \\
Piazza D'Armi, 09123 Cagliari (Italy)}
\begin{document}
\maketitle
\begin{abstract}
We used a complex network approach to study the evolution of a large software system,
Eclipse, with the aim of statistically characterize software defectiveness along the time.
We studied the software networks associated to several releases of the system,
focusing our attention specifically on their community structure, modularity and clustering coefficient.
We found that the maximum average defect density is related to two different metrics:
the number of detected communities inside a software network
and the clustering coefficient.
These two relationships both follow a power-law distribution which leads to a linear correlation
between clustering coefficient and number of communities.
These results can be useful to make predictions about the evolution of software systems,
especially with respect to their defectiveness.
\end{abstract}
\vskip 32pt
\input{introduction_ECRT}
\input{methodology_ECRT}
\input{results2_ECRT}
\input{conclusions_ECRT}
\bibliographystyle{alpha}
\bibliography{sigproc, reftest, ese_bibliography, sattose}
\end{document}