## 5 Experimental Results and Discussion
### Deep Learning Experimental Results  
Table 10 displays the experimental outcomes of the deep learning algorithms on the SFP XP-TDD data set, Eclipse Equinox data set, and Apache Active MQ Defect data set. The first column in the table lists the data set names. The term algorithm refers to the deep learning algorithm names in column 2. The metrics derived by running with the respective algorithms are ACC, AUC, FM, and KE. As depicted in Table 6, despite the outcomes obtained from deep learning algorithms with the first data set, the SFP XP-TDD were highly similar to one another; all the acquired results generated a higher classification performance rate than those of machine learning algorithms. Furthermore, the deep learning algorithm results not only outperformed all machine learning algorithm outcomes, but those outcomes were also higher than other ensemble classifier results of machine learning in terms of performance rate. The review of the Eclipse Equinox data illustrated in the second row in Table 10 indicates that the BiLSTM algorithm achieved a better performance than other algorithms, whereas this performance rate was lower than the ensemble classifier result of the SVM algorithm. However, the average deep learning algorithms' ACC success rate of 77.77% was explicitly higher than the average ACC performance rates for machine learning ensemble classifiers and base classifiers, which are both 70.64% and 76.54%, respectively. The performance rates of all algorithms posed comparable values to one another, according to the analysis of the Active MQ results displayed in Table 10's last row. In \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Data Set** & **Algorithm** & **ACC (\%)** & **AUC** & **FM** & **KE** \\ \hline \multirow{3}{*}{SFP XP-TDD} & CNN & 95.62 & 0.964 & 0.947 & 0.267 \\  & LSTM & **95.91** & 0.911 & 0.949 & 0.284 \\  & BiLSTM & 95.33 & 0.950 & 0.976 & 0.361 \\  & & 95.62 & 0.941 & 0.957 & 0.304 \\ \hline \multirow{3}{*}{Eclipse} & CNN & 76.85 & 0.839 & 0.766 & 0.508 \\  & LSTM & 77.77 & 0.837 & 0.778 & 0.538 \\  & BiLSTM & **78.70** & 0.841 & 0.787 & 0.555 \\  & & 77.77 & 0.839 & 0.777 & 0.534 \\ \hline \multirow{3}{*}{Active MQ} & CNN & **95.17** & 0.918 & 0.935 & 0.255 \\  & LSTM & 94.88 & 0.636 & 0.929 & 0.177 \\ \cline{1-1}  & BiLSTM & 94.73 & 0.718 & 0.926 & 0.130 \\ \cline{1-1}  & & 94.93 & 0.760 & 0.930 & 0.187 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Deep Learning Algorithm Based Classification results for All Data set. addition to having higher performance rates than all other machine learning algorithms, the outcomes of deep learning algorithms also exceeded the performance rates of all machine learning ensemble classifiers. There are fewer types of metrics available in the SFP-XPTDD and Eclipse data sets. However, The ActiveMQ data set comprises numerous metrics comprising unique patterns for more categories. Therefore, it seems that the LSTM and BiLSTM algorithm in the SFP-XPTDD and Eclipse data sets and the CNN algorithm, processing the discrete sections more effectively in the ActiveMQ data set, result in better outcomes. RNN Based Deep Learning (RNNBDL) Experimental Results  
Table 11 lists the results of the RNNBDL algorithm for the three separate data sets used in the study. As depicted in the results, the SFP XP-TDD, the initially employed data set, yielded higher values than those of other machine learning algorithms; however, they also attained the same value as the LSTM results, which is a deep learning algorithm. Analysis of the data related to the Eclipse, the second data set in the study, also revealed that the ACC ratio of 78.46%, achieved from the RNNBDL algorithm, was below the Rotation Forest ensemble classifier ratio of the SVM algorithm, which is a machine learning algorithm. Similarly, it lagged behind BiLSTM, a deep learning algorithm, despite the fact that the value obtained by the RNNBDL algorithm was higher than the average values acquired with both machine learning and deep learning algorithms.