## 2 Related Work  
Manual FR and NFR classification through the software requirements specification (SRS) document demand intensive effort, time, and cost. Another difficulty, on the other hand, is the uncertainty surrounding the correct classification of the identified requirements. Hence, such complications reportedly led to limited research that focused on the automatic classifying software requirements. As a result, the lack of datasets for the automatic classification of software requirements appears to be another limiting factor in this predicament. In their study, [PERSON] et al. [5] proposed a machine learning-based strategy for automatically classifying text data in the SRS document into FR and NFR formats. They performed their work on PROMISE_exp, a generic dataset, and retaining labeled requirements. They cleaned the text data in the PROMISE_exp dataset using various techniques and ran the support vector machine (SVM) and K-Nearest Neighbors (KNN) algorithms for the classification procedure. As a result, they observed that the SVM algorithm produced superior results to the KNN algorithm according to the F-measurement value in all cases. [PERSON] et al. [6] reportedly indicated that most research on classifying software requirements through machine learning algorithms was in English, with other languages receiving less attention. Hence, they created a new dataset in light of the absence of Spanish datasets. They additionally investigated which combinations of text vectorization techniques with machine learning algorithms performed best for the classification of requirements on a Spanish dataset. As a result, they found that SVM with Term Frequency-Inverse Document Frequency (TF-IDF) provided the highest F-measurement value when classifying the FR and NFR. [PERSON] and [PERSON] [7], however, developed a model in their study that potentially identified non-atomic requirements in software requirements written in natural languages. Non-atomic requirements are those for which the system has not just one function but multiple functions. If a system can fully identify features, requirements, and capabilities, it refers to an atomic requirement. An atomic requirement may be either FR or NFR. Their requirements collection was from various online sources and categorized into two separate Corpus, Corpusa and Corpusn, retaining atomic statements and non-atomic requirements, respectively. The study dataset comprised 600 requirement statements, of which 404 were from Corpusa (atomic), and 196 were from Corpusn (non-atomic) and employed Bayes Net, Random Forest, and Multilayer Perceptron machine learning algorithms for the classification process. The Bayes Net algorithm created the best model in this study, with a correct classification rate of 84.25%. The model's reliability has been deemed appropriate for unbalanced data in identifying non-atomic requirements in the software requirements specification. However, the model reliability for balance data in determining non-atomic requirements is considered moderate. Three expert reviews and the proposed model results were used for comparison and testing the model via the Cohen Kappa reliability test. On average, the proposed model displayed the highest reliability rate (0.49) [7]. [PERSON] et al. [8] conducted a study proposing a novel deep neural network model called NFRNet to extract the NFRs from requirements documents to minimize human labor and time spent and prevent mental exhaustion. They also utilized the PROMISE, a widely used dataset in software requirement classification research, increasing the NFR categories from 11 to 32 and NFR statements from 255 to 6222 in the PROMISE dataset. The NFRNet neural network they developed consisted of two parts. One was a BERT word embedding model based on N-gram masking to learn the context representation of the requirement statement, and the other was the Bi-LSTM classification network. Finally, they used the Softmax classifier to categorize the requirement statements.