## 5 Empirical Study Design
### Research Questions  
While refining the proposed model, a comprehensive roadmap was devised, elucidating detailed research questions. Crucial observations, implementations, experiments, and enhancements have been executed to address these questions.  
**RQ1:**_Does the proposed model, which involves both feature extraction and estimation techniques, contribute to the accurate estimation of SPs for the identified issues?_  
In order to examine this inquiry and to assess the viability of the suggested model combining feature extraction and estimation techniques, a thorough analysis has been carried out comparing it to conventional estimation methods (such as random guessing, mean, and median estimators) as well as four cutting-edge models (LHC-SE, LHC\({}_{\text{TC}}\)-SE, Deep-SE, and TF-IDF-SE). These models attempt to estimate the SPs of the issues from unstructured data using different techniques and approaches. This comparative analysis aims to provide a clearer understanding of the model's impact on the accuracy of SP estimation.  
**RQ2:**_Does adding other features consisting of a single word or a few words, namely 'issue type' and 'components', to the feature extraction process improve the estimation accuracy of the proposed model?_  
Additional features, including textual data represented as single words or short phrases, are integrated into the feature extraction process for further investigation. Three different variations are explored:  
1. In the first variation, only 'issue type', such as bugs, tasks, enhancements, or user stories, is added to the feature extraction process. 'Issue type' serves as the categorization or classification of a task within a system or project management framework. It  
Figure 1: The flowchart of the proposed model.  
aids in organizing and prioritizing work, providing a means to distinguish between various types of tasks within a project or system.
2. In the second variation, only 'components', such as user interface (UI), runtime, etc., referring to modular and reusable units of code or software that perform specific functions, are added to the feature extraction process.
3. In the last variation, both 'issue type' and 'components' are included in the feature extraction process.  
These variations are applied to investigate whether adding 'issue type' and/or 'components' to the feature extraction process enhances the accuracy of estimation. The goal is to understand the impact of these additional features on the semantic similarities of the issues and on the model's estimation performance.  
**RQ3:**_Does adding 'issue type' and 'components'--which consist of a single word and a few words, respectively--to the estimation process instead of the feature extraction process improve the estimation accuracy of the proposed model?_  
During the investigation, instead of integrating additional features--'issue type' and 'components'--into the feature extraction process, they are incorporated into the features extracted from 'title' and 'description_text'. Since 'issue type' is a categorical variable with unique and meaningful categories or labels that forms a predefined set of distinct categories, 'label encoding' is employed to convert it into a unique integer or numerical value. In contrast, 'components', a categorical variable with multiple categories separated by the '\(\mid\)' sign, is handled by utilizing 'one-hot encoding' to convert these categories into numerical values. This approach allows for the integration of the categorical information from 'issue type' and 'components' into the feature set derived from the 'title' and 'description_text' fields, enhancing the representation of the textual data in the analysis.