\jection{Search Based SE}
\begin{frame}
\frametitle{Rethinking Software Engineering}
Key steps to reformulating research goal as numeric optimization 
(Clarke, Harman, Jones et.\ al.\ )
\begin{itemize} 
\item representation of candidate solutions
\item objective function
\item transformation operators
\end{itemize}
\end{frame}

%\begin{frame}
%\frametitle{Representation}
%Candidate solutions have to be representated somehow
%\begin{itemize}
%\item bit vectors
%\item ASTs
%\end{itemize}
%\end{frame}

%\begin{frame}
%\frametitle{Objective function}
%The objective function must have certain characteristics
%for metaheuristic techniques to be valuable and practical
%\begin{itemize}
%\item valuable
%\begin{itemize}
%\item large input space
%\item no known analytic solutions
%\end{itemize}
%\item practical
%\begin{itemize}
%\item computationally efficient
%\item approximately continuous
%\end{itemize}
%\end{itemize}
%\end{frame}

%\begin{frame}
%\frametitle{Transformation operators}
%How are new candidate solutions formed?
%\begin{itemize}
%\item mutation operators
%\item neighborhood exploration
%\end{itemize}
%\end{frame}


\begin{frame}
\frametitle{Is SBSE a Useful Idea?}
According to a review of literature from 1992 to 2003 
evolutionary algorithms were used to solve problems from
different life stages of the software life cycle (Rela)
\begin{tabular}{|lc|}
\hline
life stage & number of publications \\
\hline
planning & 19 \\
design & 40 \\
implementation & 10 \\
testing & 54 \\
\hline
total & 123 \\
\hline
\end{tabular}
\end{frame}
