## 2 Related Work
In this study, experimental research was conducted on two widely used datasets consisting of approximately 1000 NFRs, and the results were evaluated. It has been demonstrated that the CNN model can effectively classify NFRs in both datasets, achieving an F-score ranging from 82% to 92%. A brief literature review of software requirements classification is presented in Table 1. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Title** & **Reference** & **Dataset** & **Methods** & **Results** \\ \hline Software Requirements Classification Using Machine Learning Algorithm’s. & [PERSON] et al. [5] (2021) & PROMISE\_exp & SVM, KNN & In all cases, the SVM algorithm outperforms the KNN algorithm in classifying software requirements in terms of the F-measure value. \\ \hline Towards an Automatic Requirements Classification in a New Spanish Dataset. & [PERSON] et al. [6] (2022) & New Spanish Datasets. & NB, LR, SVM, CNN, BETO & SVM with TF-IDF provided the highest F-measure value when classifying the FR and NFR. \\ \hline Detecting Non-Atomic Requirements in Software Classification Methods. & [PERSON] and [PERSON] [7] (2019) & New Dataset (Corpus,, & Bayes Net, RF, and Multilayer & The Bayes Net algorithm created the best model in this study, with a correct classification rate of 84.25\%. \\ \hline NFRNet: A Deep Neural Network for Automatic Classification of Non-Functional Requirements. & [PERSON] et al. [8] (2021) & PROMISE & BERT, Bi-LSTM, NFRNet & The NFRNet model performed the best among the other models, achieving a 91\% F-score. \\ \hline Towards Supporting Software Engineering Using Deep Learning: A Case of Software Requirements Classification. & [PERSON] et al. [9] (2017) & PROMISE & CNN & As a result of the study, it was revealed that software requirements can be classified as FR and NFR using deep learning approaches. \\ \hline CNN-BPSO Approach to Select Optimal Values of CNN Parameters for Software Requirements Classification. & [PERSON] and [PERSON] (2020) & PROMISE & CNN, CNN-BPSO & The CNN-BPSO approach achieved an 81\% accuracy in classifying software requirements, outperforming the CNN model, which had a lower accuracy of 79\%. \\ \hline Improving BERT model for requirements classification by bidirectional LSTM-CNN deep model. & [PERSON] (2023) & ProMISE & BERT-BiCNN & The study concluded that the proposed approach performs better than current deep learning methods in both binary and multi-class classification tasks. \\ \hline Non-functional Requirements Classification with Feature Extraction and Machine Learning: An Empirical Study. & [PERSON] et al. [4] (2019) & PROMISE & NB, KNN, SVM, SGD SVM DJTree, BoW, TF-IDF & In classifying NFRs, SGD SVM classifier and TF-IDF feature extraction technique gave the best performance results. \\ \hline Automatic Multi-class Non-Functional Software Requirements Classification Using Neural Networks. & [PERSON] et al. [12] (2019) & NFR dataset, PROMISE & ANN, CNN & The CNN model effectively classified NFRs in both datasets, resulting in an F-score ranging from 82\% to 92\%. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Brief literature review of software requirements classification.