\subsection{Repeatability} 

The first three experiments were based entirely on NASA projects. Even the "cross-company" data
was synthesized from other NASA data sets. To show the external validity of the conclusions found in 
these analyses, we will attempt to replicate the same analyses on three projects from a Turkish company SOFTLAB.

For each SOFTLAB project, we follow the same procedure as in the second analysis. The SOFTLAB sets 
are the "within-company" data, they are split into 90\% testing sets and 10\% testing sets.
All seven NASA projects have been combined into one large "cross-company" set. This CC data set is split into its
defective and non-defective modules, 90\% of each are combined randomly into a training set. 
Defect predictors are bult using both WC training sets and CC training sets, using both standard and 
locally weighted Naive Bayes as classifiers. This will again test whether k-NN filtering will make a difference.

The SOFTLAB projects include 29 static code features, 17 of which are common with
the NASA projects. In order to simplify the comparison between these new projects and
the NASA sets, only the shared attributes are used in this analysis. 
All available features in the SOFTLAB data sets are used in the WC analysis. 


\begin{figure}[!h]
\scriptsize
\begin{center}
\begin{tabular}{l@{~}|l@{~}|c@{~}|c@{~}|c@{~}|c@{~}|c@{~}c}
\multicolumn{2}{c}{~}&\multicolumn{5}{c}{quartiles}\\\cline{3-7}
&min& & median & &max\\\cline{3-7}
Rank&Treatment&0& 25\%& 50\% & 75\%&100\%\\\cline{1-7}
1&LWL(50) - WC&0.0& 25.0& 75.0& 95.2&100.0&  \boxplot{25.0}{75.0}{95.2}  \\
1&LWL(100) - WC&0.0& 20.0& 77.5& 97.2&100.0&  \boxplot{20.0}{77.5}{97.2}  \\
2&LWL(100) - CC&0.0& 20.0& 73.1& 93.5&100.0&  \boxplot{25.0}{73.1}{93.5}  \\
3&LWL(25) - WC&0.0& 33.3& 72.7& 93.2&100.0&  \boxplot{33.3}{72.7}{93.2}  \\
3&LWL(10) - WC&0.0& 33.3& 72.7& 92.5&100.0&  \boxplot{33.3}{72.7}{92.5}  \\
4&LWL(50) - CC&0.0& 25.0& 70.2& 90.9&100.0&  \boxplot{25.0}{70.2}{90.9}  \\
5&NB - WC&0.0& 56.5& 68.3& 83.3&100.0&\boxplot{56.5}{68.3}{83.3}\\ 
6&LWL(10) - CC&0.0& 25.0& 66.7& 89.1&100.0&  \boxplot{25.0}{66.7}{89.1} \\
7&LWL(25) - CC&0.0& 25.0& 66.7& 88.9&100.0&  \boxplot{25.0}{66.7}{88.9}  \\
8&NB - CC&0.0& 47.2& 64.9& 87.5& 100.0& \boxplot{47.2}{64.9}{87.5} \\
\end{tabular}
\end{center}
\caption{Probability of Detection (PD) results for Naive Bayes on within vs cross-company data,
sorted by median values. [Placeholder]
}\label{fig:an2PD}
\end{figure}

\begin{figure}[!h]
\scriptsize
\begin{center}
\begin{tabular}{l@{~}|c@{~}|c@{~}|c@{~}|c@{~}|c@{~}c}
\multicolumn{2}{c}{~}&\multicolumn{5}{c}{quartiles}\\\cline{3-7}
&min& & median & &max\\\cline{3-7}
Rank&Treatment&0& 25\%& 50\% & 75\%&100\%\\\cline{1-7}
1&LWL(100) - WC&0.0& 2.7& 22.2& 80.0&100.0&  \boxplot{2.7}{22.2}{80.0}  \\
1&LWL(50) - WC&0.0& 4.7& 22.2& 75.0&100.0&  \boxplot{4.7}{22.2}{75.0}  \\
2&LWL(100) - CC&0.0& 6.5& 26.2& 80.0&100.0&  \boxplot{6.5}{26.2}{80.0}  \\
3&LWL(25) - WC&0.0& 6.7& 25.0& 66.7&100.0&  \boxplot{6.7}{25.0}{66.7} \\
4&LWL(10) - WC&0.0& 7.3& 27.3& 66.7&100.0&  \boxplot{7.3}{27.3}{66.7}  \\
5&LWL(50) - CC&0.0& 9.1& 28.7& 75.0&100.0&  \boxplot{9.1}{28.7}{75.0}  \\
6&NB - WC&0.0& 16.7& 31.6& 43.2&100.0& \boxplot{16.7}{31.6}{43.2}\\ 
7&LWL(10) - CC&0.0& 10.8& 33.3& 74.3&100.0&  \boxplot{10.8}{33.3}{74.3} \\
8&LWL(25) - CC&0.0& 10.6& 33.0& 75.0&100.0&  \boxplot{10.6}{33.0}{75.0}  \\
9&NB - CC&0.0& 11.1& 34.8& 52.6&100.0& \boxplot{11.1}{34.8}{52.6}\\ 
\end{tabular}
\end{center}
\caption{Probability of False Alarm (PF) results for Naive Bayes on within vs cross-company data,
sorted by median values.[placeholder]
}\label{fig:an2PF}
\end{figure}


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