## 2 Related Work
They applied a novel editing method for the model training process known as multi-sample dropout to potentially reduce the number of training iterations needed, accelerate the training of deep neural networks, and maintain reduced error rates in the trained networks. Furthermore, they employed the Tenfold cross-validation technique on the SOFTWARE NFR dataset to test the proposed model's classification accuracy; accordingly, the NFRNet model indicated the highest performance, with 91% precision, 92% recall, and 91% F-score among the other models they used [8]. [PERSON] et al. [9] reportedly asserted that software requirements can be classified using deep learning approaches. They accordingly proposed a model to analyze requirements documents for large software projects via natural language processing techniques. The basis of their proposed model is the Convolutional Neural Network (CNN), one of the deep learning algorithms. Consequently, they evaluated their proposed model using the PROMISE dataset, including FR and 11 different NFR-labeled requirements. As a result, they stated that software requirements can be classified using deep learning approaches. [PERSON] and [PERSON] [10] proposed a CNN model to classify software requirements as FR and NFR. The CNN retained several hyperparameters that affect prediction performance, such as filter size, number of filters, input insertion size, and CNN architecture. Their study aimed to optimize the CNN parameters for better prediction accuracy using the Binary Particle Swarm Optimization (BPSO) approach. They also utilized PROMISE as a study dataset, comprising 538 labeled FR and NFR. Initially designing a CNN model to classify the software requirements into FR and NFR, they subsequently employed the pre-trained dataset using the bag-of-words (BOW) technique and Wikipedia for preprocessing, in other words, converting the text data into a vector of numerical data. Finally, they developed the CNN-BPSO model to optimize the CNN hyperparameters using BPSO. Considering the experimental results, the proposed CNN-BPSO approach--80% training and 20% test data--for classifying software requirements resulted in an 81% accuracy value, outperforming the CNN model, which retained a 79% accuracy value [10]. [PERSON] and [PERSON] [11] proposed a deep learning-based BERT-BiCNN model, which integrates the BERT algorithm with RNN-CNN layers to improve performance in requirements classification. Experimental studies for the proposed model were carried out on the PROMISE dataset. It was concluded that the proposed approach outperforms current deep learning approaches in binary and multi-class classification. The literature review revealed several studies focused on the correct classification of NFR, which played a critical role in improving the software quality. In this context, [PERSON] et al. [4] reportedly emphasized that accurate NFR extraction is crucial in high-quality software development. They further indicated that the presence of the FR and NFR in the same SRS document leads to confusion; thus, differentiating these requirements would require considerably more effort. They also proposed an approach for the automatic NFR classification by combining machine learning feature extraction and classification techniques. Accordingly, they performed an experimental study using seven machine learning algorithms and four feature selection approaches. They additionally strived to identify the best pair for automatic classification based on the statistical analysis results of the experimental studies. Therefore, they stated that the stochastic gradient descent support vector machine (SGD SVM) classifier and TF-IDF (character level) feature extraction technique delivered the best outcomes. [PERSON] et al. [12] proposed the use of Artificial Neural Networks (ANNs) and CNN deep learning models to classify non-functional requirements into five categories: operability, maintainability, security, performance, and usability.