## 2 Related Work
The proposed system underwent fine-tuning for crucial hyperparameters, including the number of word-embedding dimensions and the number of hidden layers in the RHWN component. Evaluation utilized a dataset comprising 23,313 issues sourced from nine major open-source repositories through the JIRA issue tracking system. Deep-SE was compared with baseline methods (mean and median), as well as other models like RF, SVM, automatically transformed linear model (ATLM), and linear regression model (LRM) coupled with LSTM, based on metrics such as MAE, MdAE, and SA. The results demonstrated that Deep-SE significantly outperformed both baseline methods and the model proposed by [PERSON] et al. [27] in terms of MAE. [PERSON] and [PERSON] [104] conducted a comprehensive evaluation study of Deep-SE [30] using a dataset comprising 4727 user stories extracted from a commercial project. The project involved a 27-member Agile team working on a data anonymization product at IQVIA, a healthcare data science company. The study aimed to assess the generalizability of Deep-SE in non-open-source environments. Comparing its results with baseline models revealed that Deep-SE significantly outperformed these models, showcasing its effectiveness with proprietary data. Importantly, the findings suggested that Deep-SE was versatile and applicable not only within the confines of a single company, but also in cross-company scenarios. In their replication study, [PERSON] et al. [105] sought to investigate the efficacy of Deep-SE for both within-project and cross-project effort estimation. The study utilized the Deep-SE model along with an additional dataset consisting of 31,960 issues obtained from TAWOS [35]. Deep-SE was compared with three baseline estimators (random, mean, and median) and a previously proposed method for ASD effort estimation named TF/IDF-SVM [27]. Contrary to previous findings on the effectiveness of Deep-SE, the replication results indicated that Deep-SE only outperformed the median baseline estimator and TF/IDF-SVM in very few cases with statistical significance. This divergence in results added complexity to the understanding of Deep-SE's performance across different scenarios. [PERSON] et al. [31] introduced an innovative approach for SP estimation by leveraging the textual features of issues. This method utilized both topic modeling and clustering techniques to enhance prediction accuracy. Latent Dirichlet allocation (LDA) was employed to extract topic similarities from the textual content found in titles and descriptions. Additionally, hierarchical clustering was applied to group issues based on their topic similarities. Subsequently, three estimation models were investigated within each cluster: cluster-mean-based estimation, which provides the mean SP of all issues in the cluster; cluster-median-based estimation, which offers the median SP of all issues in the cluster; and closest point-based estimation, returning the SP of the closest issue to the new incoming issue. Evaluation of the proposed approach involved a dataset comprising 31,960 issues from 26 projects across 13 different open-source repositories. Comparative analysis against baselines (random guessing [106] and mean and median method [19; 107; 108]) and two state-of-the-art models, Deep-SE [30], and TF-IDF-SE [27], was performed using metrics such as MAE, MdAE, and SA. The results demonstrated the superiority of the proposed approach over both baselines and state-of-the-art models. [PERSON] et al. [109] proposed GPT2 SP, a transformer-based Agile SP estimation approach that leveraged a transformer-based architecture to enhance SP estimation. The model used a GPT-2 pre-trained language model, enabling it to capture the relationships among words while considering the context surrounding each word and its position in the sequence. Comparative analysis using MAE demonstrated significant improvements in estimation accuracy for both within-project and cross-project scenarios, outperforming other baseline approaches, including Deep-SE.