## 4 Result and Analysis
### Results and Analysis
To experiment, we combined all the versions of the same dataset as one project and treated it as the source. We then tested one source project against all the versions of 28 projects from the promise repository. We combined the versions to get a large range of data so that we could efficiently train our classifier. By combining the versions of one project, we also obtained the maximum range of the project to train our classifier. Table 4 shows the results we obtained from our experiment. We have highlighted the highest accuracy of one project in bold in every row. In Table 4, where source and target are same, we put dashes at their intersection point because we aren't addressing the issue of WPDP. As our experimental domain is cross-project defect prediction, we did not predict the results for the same target projects. From our experimental results, it is evident that through hybrid feature selection we obtained the optimal set of features. By combining the SoftMax layer with the NN layer, better prediction accuracies were achieved for a multi-class dataset in terms of AUC measure for the PROMISE repository, which supports our alternative hypothesis, \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{10}{c}{**AUC Measure**} \\ \hline  & \multicolumn{8}{c}{**Source**} \\ \hline
**Target** & **Ant** & **Camel** & **Jedit** & **Lucene** & **Poi** & **Log4j** & **Velocity** & **Synapse** & **Ivy** & **Xalan** & **Xerces** \\ \hline Jedit-4.0 & 86.05\% & 77.30\% & – & 76.90\% & 64.95\% & 62.33\% & 65.40\% & 84.83\% & 82.67\% & 64.50\% & 53.62\% \\ \hline Jedit-4.1 & 80.75\% & 71.28\% & – & 66.51\% & 65.26\% & 58.24\% & 48.99\% & 71.39\% & 82.65\% & 77.54\% & 58.00\% \\ \hline Log4j-1.0 & 80.68\% & 76.86\% & 73.98\% & 75.23\% & 71.62\% & – & 66.16\% & 64.75\% & 73.01\% & 65.80\% & 60.53\% \\ \hline Log4j-1.1 & 86.09\% & 77.48\% & 75.36\% & 78.90\% & 69.61\% & – & 62.16\% & 72.47\% & 58.60\% & 71.52\% & 60.10\% \\ \hline Log4j-1.2 & 61.99\% & 66.25\% & 58.96\% & 65.82\% & 53.89\% & – & 50.55\% & 59.68\% & 49.38\% & 56.86\% & 48.24\% \\ \hline Lucene-2.0 & 74.84\% & 69.11\% & 63.11\% & – & 66.82\% & 56.62\% & 57.70\% & 71.58\% & 75.04\% & 65.88\% & 54.75\% \\ \hline Lucene-2.2 & 65.98\% & 61.37\% & 66.38\% & – & 66.24\% & 61.59\% & 55.50\% & 65.02\% & 66.61\% & 60.47\% & 55.59\% \\ \hline Lucene-2.4 & 68.31\% & 64.91\% & 66.50\% & – & 62.42\% & 66.92\% & 58.11\% & 63.65\% & 62.04\% & 62.21\% & 53.58\% \\ \hline Synapse-1.0 & 88.08\% & 65.65\% & 64.03\% & 65.82\% & 78.39\% & 49.82\% & 59.50\% & – & 77.69\% & 75.36\% & 49.59\% \\ \hline Synapse-1.1 & 74.45\% & 71.04\% & 74.07\% & 64.73\% & 69.38\% & 57.49\% & 57.98\% & – & 73.42\% & 62.96\% & 63.15\% \\ \hline Synapse-1.2 & 72.65\% & 65.63\% & 61.25\% & 62.15\% & 65.05\% & 59.05\% & 61.39\% & – & 72.36\% & 66.82\% & 58.91\% \\ \hline Velocity-1.4 & 61.64\% & 53.99\% & 59.91\% & 63.86\% & 61.13\% & 64.92\% & – & 66.89\% & 63.72\% & 59.26\% & 58.75\% \\ \hline Velocity-1.5 & 68.99\% & 60.81\% & 77.47\% & 73.42\% & 68.40\% & 61.90\% & – & 71.65\% & 65.47\% & 64.60\% & 64.43\% \\ \hline Velocity-1.6 & 56.93\% & 55.34\% & 57.90\% & 58.05\% & 63.65\% & 48.25\% & – & 65.30\% & 63.87\% & 51.56\% & 51.45\% \\ \hline Xalan-2.4 & 72.63\% & 69.76\% & 69.86\% & 65.40\% & 67.00\% & 57.75\% & 56.21\% & 69.51\% & 76.31\% & – & 64.72\% \\ \hline Xalan-2.5 & 79.00\% & 78.57\% & 74.75\% & 69.07\% & 73.39\% & 58.82\% & 55.21\% & 74.44\% & 78.51\% & – & 60.88\% \\ \hline Xalan-2.6 & 77.25\% & 70.32\% & 71.56\% & 68.36\% & 64.83\% & 59.30\% & 51.37\% & 73.86\% & 76.62\% & – & 67.41\% \\ \hline Xerces-1.2 & 63.35\% & 64.73\% & 60.31\% & 55.35\% & 60.20\% & 54.14\% & 53.95\% & 65.14\% & 66.32\% & 54.75\% & – \\ \hline Xerces-1.4 & 63.71\% & 64.76\% & 64.55\% & 63.91\% & 61.74\% & 62.79\% & 50.11\% & 59.23\% & 60.14\% & 64.96\% & – \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._i.e., \"Hybrid feature selection selects optimal feature sets for multi-class in predicting cross-project defect prediction accuracy of PROMISE repository\".