## 2 Related Work
The results indicate that the extracted feature set is effective at achieving good accuracy and that their model performs as well as, if not better than, developers' subjective estimates, especially for large datasets. [PERSON] et al. [27] introduced an ML classifier designed to estimate the SPs necessary for addressing an issue. They conducted feature extraction from 4908 issues sourced from one industrial project and eight open-source projects. The features included the issue type, related components, and term-frequency inverse document frequency (TF-IDF). Issues from the industrial project were obtained through JIRA, a widely used issue-tracking system, with the TF-IDF being derived from the summary and description fields. These features were employed in SVM, KNN, DT, and NB classifiers, chosen for their suitability in text classification applications. To validate their ML classifiers, a 10-fold cross-validation was applied, and performance metrics such as mean magnitude of relative error (MMRE) and accuracy were assessed. Their results highlight that fields containing the summary, description, names of related components, and issue type in an issue report provide relevant features for accurate SP estimation. Furthermore, their findings suggest that training the classifiers with more than 200 issues is preferable in order to ensure a stable model with satisfactory accuracy before deploying it for SP estimation. [PERSON] et al. [28] embarked upon another classification task to estimate SPs. The authors undertook feature extraction encompassing both developer-based and text-based features from 4142 user stories sourced from eight open-source projects. Developer-based features included developers' reputation, current workload, total number of issues, number of developer comments, and total SPs. Text-based features comprised context, number of characters, and n-grams essential for calculating TF-IDF. The features were utilized to train an SVM for classification. The performance of their predictive model was evaluated using a 10-fold cross-validation and metrics such as accuracy, MAE, and SA. Their findings highlighted a slight out-performance of models trained with developer-based features compared to those trained solely with text-based features. Additionally, the results indicated that using an SVM classifier and exclusively relying upon developer features for predicting SPs surpass the performance achieved by using textual features alone or a combination of textual and developer features. In a distinctive classification pursuit, [PERSON] [29] conducted a study using four diverse variants of auto-encoder networks in order to estimate the SPs of user stories by relying upon their semantic similarities obtained from textual data contained in issue summaries. The author also fine-tuned these four variants through an optimization process facilitated by randomized search. Intriguingly, the study's outcomes revealed no discernible performance disparity among these four variants, which was attributed to the inherent semantic simplicity of user stories. Evaluation of the study encompassed a dataset featuring 3439 issues extracted from six open-source Agile projects and employing rigorous 10-fold cross-validation for each model variant. [PERSON] et al. [30] introduced an end-to-end prediction system, named Deep-SE, designed to estimate SPs for user stories. The system leverages the synergy of two potent DL architectures: long short-term memory (LSTM) and recurrent highway network (RHWN). The textual information from user story titles and descriptions was amalgamated into a unified text representation, with each text segment being transformed into a vector representation through the LSTM layer. Subsequently, a differentiable regressor was employed for the immediate prediction. The RHWN assisted in mapping the deep representation of the vector extracted from the LSTM layer. Finally, a linear activation function in a feed-forward neural network served as the ultimate regressor for producing the SP estimation.