\section{Motivation}
\label{sect:motivation}

%In the early stages of this research, due to two very different views , we were skeptical about the performance of multi-methods.
%That is due to the fact that we have 2 very different views regarding the performance of multi-methods.
%The first view is from SE area: It is reported in~\cite{Khoshgoftaar2009} and \cite{Kocaguneli2009} that multi-methods perform poorly on SE datasets due to their particular characteristics.
%The second view is from ML area, where multi-methods are shown to be successful in practical applications~\cite{Kittler1998, Ghosh2002} as well as  in theoretical settings~\cite{Alpaydin1998, Dietterich2000, Ghosh2002}.

This research is based on the lessons learned from previous work~\cite{Khoshgoftaar2009,Kocaguneli2009,Alpaydin1998,Dietterich2000,Ghosh2002}.
The work in SE domain reports that multi-methods perform poorly~\cite{Khoshgoftaar2009,Kocaguneli2009}.
On the other hand machine learning (ML) community perspective is that multi-methods are successful learners in practical~\cite{Kittler1998, Ghosh2002} as well as in theoretical settings~\cite{Alpaydin1998, Dietterich2000, Ghosh2002}.

The contradicting results urged us to investigate the reasons behind them.
One of the differences we identified were the schemes used to build multi-methods.
When constructing a multi-methods, it is particularly useful if there is diversity in the learning process~\cite{Kittler1998, Alpaydin1998}.
This can be achieved by 3 factors: 1) different learners, 2)different feature subsets and 3) different training sets.
In this section, we suffice to say that in SE literature, these factors were not deeply investigated.
We provide a deeper discussion on that issue in \tion{discussion}.

Another motivating factor for us came during the preliminary analysis.
%We wanted to see the performance of all methods on a dataset at instance level.
%When we plotted the magnitude of relative error (MRE) values on Nasa93 dataset for 
In \fig{nasa93-mre-values} cells represent the performance of methods (y-axis) on individual instances (x-axis) in terms of MRE (for MRE see \tion{methodology}): Different MRE intervals are represented with different colors.
Note that all intervals can occur along a line $x=i$ where $i \in \left\lbrace 1, ...,102 \right\rbrace$:
\begin{quote}
The key observation is that best estimates for a project are given by different methods.
%This tells us that the best estimate for a project is given by different methods.
\end{quote}
We can make use of this fact by combining the estimations of \textit{stably-successful} solo-methods.
More details on the definition and combination of \textit{stably-successful} solo-methods will be provided in \tion{methodology}.

%Although previous SE literature on multi-method applications were not encouraging, we were able to find motivation for this research from multiple factors.
%To begin with, in some other domains there are both theoretical and practical evidence that multi-methods work better than single methods.
%Therefore, in the worst case scenario we would have provided more evidence that multi-methods perform poorly in SE domain.
%Secondly, when we questioned why there was such a performance differences concerning multi-methods performance, we came across some experimental setting differences.
%When constructing a multi-methods scheme, it is particularly useful if there is diversity in the learning process~\cite{Kittler1998, Alpaydin1998}.
%This can be achieved by 3 factors: 1) different learners, 2)different feature subsets and 3) different training sets.
%In this section, we suffice to say that in SE literature, these factors were not deeply investigated.
%We provide a deeper discussion on that issue in \tion{discussion}.
%Third and the last factor of motivation was some preliminary analysis.
%We wanted to see the performance of all methods on a single dataset at instance level.
%When we plotted the magnitude of relative error (MRE) values on Nasa93 dataset for all methods, we got the plot in \fig{nasa93-mre-values}.



\begin{figure*}
\centering
\includegraphics[width=1\textwidth]{plots/nasa93MREValues.pdf}
\caption{The MRE values of all the methods for Nasa93. 
The lines along y-axis represent how the MRE values of a single instance change with respect to different methods. The fact that lines are mostly multi-colored means that different instances are best estimated by different methods. The order of methods in y-axis is given in \fig{algorithm-ranks} and the instances on the x-axis are sorted from the lowest sum of MRE over 102 methods to the highest.}
\label{fig:nasa93-mre-values}
\end{figure*}