## 8 Threats to Validity  
In this study, models and methods were selected and utilized to develop the proposed model based on the dataset obtained from 26 open-source Agile projects. These models and methods might not generalize well to all software development projects. Different projects can have unique characteristics, such as different domains, team sizes, and complexity levels, which might affect the applicability of the findings. The proposed model was trained, validated, and tested with data from real-world projects in different domains in order to minimize threats to external validity. Nevertheless, it cannot be claimed that the proposed model is well-suited to all ASD projects, especially industrial software development projects.  
Another threat to external validity is dataset limitations. The findings of the proposed model are based on the dataset used. The dataset cannot be representative of the broader spectrum of software development projects, especially industrial software development projects; thus, the results might not be applicable to other contexts. Furthermore, issue reports may be written in a more disciplined and detailed environment. Therefore, further investigation is required to validate this study's conclusions using datasets obtained from industrial software organizations.  
Feature representation is a threat to construct validity. The impact of the choice of features and their representation on the model's performance is recognized. This study used embedding vectors from the titles and descriptions of issues obtained using powerful transformers inside SBERT as features. However, there might be other relevant features and representation methods that could lead to different results. To address this, different variations in the feature extraction process were used, and the results of each variation were compared to select the more accurate model.  
Other threats affecting conclusion validity include the statistical significance and effect size interpretation of the results. The study ensured that the statistical methods used to compare the models were robust and appropriate for the data. Inappropriate statistical methods could lead to incorrect conclusions. To prevent this, robust and well-established statistical methods were employed to compare the models. These statistical methods included using standard performance metrics like MAE and MdAE, which are widely recognized in the field. In addition to this, the effect size used in this study to interpret the results was carefully considered. Interpreting effect sizes, especially compared to state-of-the-art models, requires careful consideration. The practical significance of these effect sizes was evaluated in the context of real-world software development. To minimize this threat to conclusion validity, effect sizes were interpreted with a focus on their practical significance in real-world software development scenarios, and effect sizes were contextualized within the specific challenges and needs of software project management rather than relying solely on statistical significance.  
Finally, this study's methodology, which encompassed text extraction, data preprocessing, feature extraction, model training, evaluation, validation, and comparison with standard and state-of-the-art models, was thoroughly documented. This documentation aimed to facilitate reproducibility by other researchers, ensuring that the methods can be replicated and the results validated.