## 7 Discussion  
This study embarked on the ambitious task of enhancing the accuracy of SP estimation for software development issues by implementing and evaluating various models. The results demonstrate that leveraging the synergistic potential of SBERT for feature extraction and GBT models for estimation significantly improves the accuracy of SP predictions. Notably, the SBERT-LGBMTC model, augmented by manual optimization techniques, emerged as an effective estimator, achieving the lowest MAE and MdAE values, as well as the highest SA across several evaluation metrics. Notably, the **SBERT-LGBMTC** model, augmented by manual optimization techniques, emerged as a highly effective estimator, outperforming state-of-the-art models by achieving the lowest MAE and MdAE values as well as the highest SA. This finding highlights the quantitative advantage of the proposed model in addressing the complexities of software development tasks. The practical significance of the metrics used--MAE, MdAE, and SA--lies in their ability to provide a comprehensive evaluation of the model's predictive performance. MAE and MdAE offer insights into the model's accuracy in predicting SPs, with lower values indicating better performance in capturing typical and median errors, respectively. SA, meanwhile, assesses the model's capability to outperform random guessing, with higher values reflecting enhanced reliability in practical scenarios. By combining these metrics with effect size, this study not only identifies statistically significant differences but also evaluates the real-world impact of these differences, ensuring that the proposed model delivers tangible benefits in software project management. The use of the **SBERT-LGBMTC** model in this study for generating structured embedding vectors from texts, including issue titles and descriptions, can be shown to be an effective method for capturing semantic similarities between issues. Incorporating robust and powerful transformers, the model efficiently condenses complex textual information into a structured format conducive to ML models, particularly GBTs. Additionally, this study confirms the effectiveness of GBT models in handling structured data. Fine-tuning these models further enhances their performance, with the **SBERT-LGBMTC** model with manual tuning standing out due to its low MAE and high SA. The comparative advantage of the proposed model is underscored by its superior performance over standard estimation methods, such as random guessing and mean and median estimators. This advantage becomes particularly evident in ASD projects, where conventional methods often fall short due to inherent complexity. Moreover, in comparison with other state-of-the-art models, the proposed model's effectiveness within the current technological landscape is demonstrated. Enhanced performance, with a 15% reduction in MAE and a 10% reduction in MdAE compared to Deep-SE in several projects, indicates a superior ability to handle the complexities inherent in software development tasks, implying that the features and models employed are adept at capturing the nuanced requirements of these projects. When evaluated against LHC-SE and its variant, LHCTC-SE, the proposed model's robustness was further affirmed. Its consistent outperformance across a diverse range of projects highlights the adaptability and accuracy of the proposed model. Compared to TF-IDF-SE, the proposed model demonstrated superior effectiveness in understanding and interpreting textual data in issue descriptions and titles, underscoring the strength of this study. The strength of the proposed model lies in its comprehensive and nuanced analysis of text data using advanced NLP techniques coupled with the deployment of sophisticated ML models. This dual strategy facilitates a deeper understanding and more accurate estimations of complex software development tasks.