## 6 Results  
**RQ1:**_Does the proposed model, which involves both feature extraction and estimation techniques, contribute to the accurate estimation of SPs for the identified issues?_  
As described in Section 4, the main objective of this study is to find semantic similarities between user stories/issues and to predict the SP value of a newly incoming user story/issue. To achieve this, the structured data, namely embedding vectors, which provide a dense, lower-dimensional representation of the text data, were obtained. These embedding vectors were generated using the powerful transformers of the SBERT model applied to the unstructured data. The structured data, comprised of 768 features, were derived from the text information found in the titles and descriptions of 26 project issues. Subsequently, three GBT models--CatBoost, XGBoost, and LightGBM--all of which are well-suited for handling structured data, were implemented. Before comparing these implemented models with four state-of-the-art models, random guessing, and mean and median estimators, fine-tuning was performed on these models using various techniques such as manual tuning, Bayesian optimization, and random search. After fine-tuning, an initial comparison was conducted among the implemented models based on key evaluation metrics, including MAE, MdAE, and SA, to determine which models yielded the best results. Based on the results presented in the first line of Table 3's first row, several observations can be made regarding each of the three GBT models. First, the **SBERT-CAT** model exhibits notably strong performance when utilizing the feature set extracted from the titles and descriptions of issues. This is particularly evident when applying Bayesian optimization and random search for hyperparameter tuning, resulting in significantly low MAE and high SA. Second, the **SBERT-XG** model also demonstrates impressive results when using the feature set derived from issue titles and descriptions, especially when Bayesian optimization is applied. It achieves competitive performance in terms of MAE and SA. Last, the **SBERT-LGBM** model showcases strong results, particularly when utilizing the feature set extracted from issue titles and descriptions with Bayesian optimization, leading to high SA. As a result, after a thorough evaluation, the **SBERT-XG** model with Bayesian optimization emerges as the preferred choice for this variation of the study. This model displays exceptional performance in terms of MAE, MdAE, and SA when utilizing the feature set extracted from issue titles and descriptions. Its ability to achieve lower MAE and MdAE along with higher SA positions it as the top-performing model among the three GBT models. Subsequently, the **SBERT-XG** model with Bayesian optimization was compared to four state-of-the-art models, random guessing, and mean and median estimators. This comparative analysis aims to assess how the selected model stacks up against established models in the field. Table 4 showcases MAE, MdAE, and SA values attained by the **SBERT-XG** model with Bayesian optimization compared to four state-of-the-art models as well as random guessing and mean and median estimators. The results indicate that the SA values for the projects CONFCLOUD, CONFSERVER, DURACLOUD, EVG, and SERVER are negative, suggesting areas for potential enhancement, while for the remaining projects are positive, demonstrating the **SBERT-XG** model's superiority over random guessing in 21 projects. \begin{table}
\begin{tabular}{c c} \hline \hline
**Cohen’s d Value** & **Interpretation** \\ \hline \(d=0.2\) & Small Effect \\ \hline \(d=0.5\) & Medium Effect \\ \hline \(d=0.8\) & Large Effect \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Interpretation of Cohen’s \(d\) effect sizes.