## 3 Methodology
The minimum and maximum limits are '\(-\)1' and '\(+\)1', respectively, meaning that the performance increases and the predictions are correct as the value approaches '1.' \[\text{MCC}=\frac{\left(\text{TP}*\text{TN}\right)-\left(\text{FP}*\text{FN} \right)}{\sqrt{\left(\left(\text{TP}+\text{FP}\right)*\left(\text{TP}+\text{ FN}\right)*\left(\text{TN}+\text{FP}\right)*\left(\text{TN}+\text{FN} \right)\right)}} \tag{4}\]

Kappa statistic indicates the correspondence between the actual and predicted values. It also considers whether this correspondence is by chance. As in the MCC metric, the minimum and maximum values vary between '\(-\)1' and '\(+\)1', respectively. As a result, the

Figure 6: Confusion matrix. model is more successful as the Kappa value approaches \"1\" [48], whereas \"0\" indicates that the existing correspondence is by chance. ## 4 Experiments and Discussion

This section explores the answers to the following research questions (RQ1, RQ2, RQ3, and RQ4):

* RQ1: How successful are conventional supervised learning methods in identifying software requirements as functional and non-functional? * RQ2: How successful are deep learning methods in identifying software requirements as functional and non-functional? * RQ3: How successful are transfer learning models in identifying software requirements as functional and non-functional? * RQ4: Which of the conventional supervised learning, deep learning, and transfer learning methods is more successful in classifying software requirements? ### Procedure Followed in Experiments

In principle, experimental studies involve three stages: machine learning algorithms, deep learning algorithms, and classification of transfer learning methods and software requirements as FR and NFR. All of the experiments utilized in the dataset are provided in Section 3. While performing experimental studies, the dataset was divided into two parts, 80% and 20% for training and testing, respectively. ### Experimental Results

The study initially used the Weka tool for classification experiments with machine learning algorithms. It also experimented with all machine learning algorithms available on Weka and revealed the findings of the 11 most successful algorithms provided in Figure 3:0

The data analysis presented in Table 3 revealed that the models developed with machine learning algorithms yielded comparable performance results. Of all the models, the model developed with the Naive Bayes Multinomial was the highest-performing model, with a 92% F-score and 97% AUC value. The model developed by the LMT algorithm followed the Naive Bayes Multinomial algorithm with a 91% F-score and 95% AUC value. In addition, the models created by the Logistic Regression and Multiclass Classifier displayed the same performance level. Considering the algorithm with the lowest performance among the 11 algorithms analyzed, however, it was the model developed by J48, with an 82% F-score and 87% AUC value. In addition to conventional machine learning algorithms, feature selection techniques have been applied to the same algorithms to improve performance. In this context, the study used CFS and GR feature selection methods to identify their effects on performance results and displayed the outcomes in Figure 3:1 \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **F-Score** & **AUC** \\ \hline NB & 0.830 & 0.899 \\ LMT & 0.914 & 0.959 \\ RF & 0.909 & 0.961 \\ SLR & 0.899 & 0.956 \\ JRip & 0.901 & 0.887 \\ NBM & 0.928 & 0.970 \\ SMO & 0.913 & 0.902 \\ LR & 0.888 & 0.933 \\ Bagging & 0.863 & 0.930 \\ J48 & 0.826 & 0.879 \\ Multiclass Classifier & 0.888 & 0.933 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: F-score and AUC value of conventional algorithms. The performance results of the feature selection algorithms in Table 4 indicated that they failed to generate any positive effect on the performance increase.