## 9 Conclusions and Future Work  
This paper introduces a novel DL- and ML-based model that effectively combines SBERT for nuanced feature extraction with advanced GBT algorithms. The primary aim behind this model is to detect semantic similarities between issues and estimate the SP for new issues by transforming unstructured text data, such as titles and descriptions, into structured data which can then be utilized to assess the SP of incoming issues. To evaluate the effectiveness of the proposed model, an empirical study was conducted comparing it to standard estimation methods such as random guessing and mean and median estimators as well as to state-of-the-art models including LHC-SE, LHC-SE, Deep-SE, and TF-IDF-SE. This comparison, which involved data from 26 current open-source Agile projects, highlighted the robustness and adaptability of the proposed model across various software project datasets. The results showcased the **SBERT-LGBMTC** model's superiority in accurately estimating efforts compared to conventional models; it achieved a 15% reduction in MAE and a 10% reduction in MdAE. This research not only significantly contributes to the academic understanding of SEE but also offers practical tools that can e used as decision-support systems for project managers and software development teams. By providing a reliable method for estimating project efforts and resources, the proposed model addresses the critical challenges in managing the dynamic and often unpredictable landscape of Agile projects. It is important to note, however, that in 4 out of the 26 projects evaluated, the model did not outperform other methods, which indicates potential areas for further refinement. The practical relevance of NLP- and ML-based estimates is crucial to their application in real-world scenarios. These automated techniques can process large amounts of data quickly, providing consistent and objective estimates that reduce human error and bias. However, despite their advantages, NLP- and ML-based estimates have not completely replaced manual or expert-based estimates due to the complexity and contextual nuances of certain projects. The proposed model is suggested as a valuable supplementary tool rather than a replacement for expert judgment, enhancing the final estimates made by human experts with quick, data-driven insights. For practical application, it is crucial to establish protocols for integrating NLP- and ML-based estimates into existing estimation processes and to enhance the utility of these tools through training and guidelines for experts on how to effectively use them. Looking forward, several avenues for further research could enhance the performance and applicability of the **SBERT-LGBMTC** model:  
* **Integration of Contextual and Qualitative Data:** Future studies could explore incorporating additional contextual information, such as team experience, project complexity, and technological novelty. Qualitative data, such as stakeholder feedback and team dynamics, might also provide valuable insights that could refine the estimation process, echoing the complexities and challenges of implementing Scrum in a global software development context discussed in the paper. * **Cross-Industry Data Integration for Agile Estimation Models:** Incorporating datasets from various software development sectors, such as healthcare, finance, and entertainment, could significantly enhance the ability of the **SBERT-LGBMTC** model with Bayesian optimization to generalize across industry-specific requirements and terminologies. This integration would help refine the model's accuracy by adapting to different sectors' unique challenges and practices, promoting a more versatile application of Agile estimation techniques. * **Enhancing Data Collection through Global Collaboration:** Promoting global collaborative efforts to gather a more comprehensive dataset including Agile projects from diverse geographical locations could provide invaluable insights into regional differences in project management and execution.