## 4 Experimental Setup
### Datasets  
#### 4.2.1 Project Selection  
For our experiment, we selected open-source projects publicly available on GitHub. The reason for choosing open-source projects on GitHub is that GitHub provides an integrated issue management system, and users frequently submit issue reports on this platform. We investigated active projects on GitHub and sorted them based on the number of issue reports. After sorting many projects based on the number of issues, we manually investigated the top 100 projects. Among these projects, we investigated the labels attached to issue reports, and we chose projects that maintain 'bug' and 'feature' labels or the corresponding labels. We specifically chose projects that had issue reports labeled as either 'bug' or 'feature' since our proposed model aims to perform binary classification of issue reports into these classes.  
Additionally, we verified whether issue reports simultaneously contained text, image, and code because we considered multimodal techniques for issue classification (as shown in Figure 1). Selecting issue reports that contain all three types of data is important to examine the impact of each data type on issue classification for the same issue report.  
We finally chose the top four projects that met our selection criteria. The four projects are VS Code, Kubernetes, Flutter, and Roslyn. Table 1 presents the number of issue reports per label for each project on GitHub. The total number of issue reports for each selected project is as follows: VS Code (160,218 issues), Kubernetes (115,035 issues), Flutter (118,576 issues), and Roslyn (66,464 issues). Among them, the number of issue reports with the labels 'bug' or 'feature' is as follows: VS Code (48,427 issues), Kubernetes (18,243 issues), Flutter (23,004 issues), and Roslyn (16,706 issues).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Project** & **Total Number of Issues** & **Label** & **Num** \\ \hline \multirow{3}{*}{VS Code} & \multirow{3}{*}{160,218} & Bug & 28,353 \\  & & Feature & 20,074 \\  & & Total & 48,427 \\ \hline \multirow{3}{*}{Kubernetes} & \multirow{3}{*}{115,035} & Bug & 13,059 \\  & & Feature & 5184 \\  & & Total & 18,243 \\ \hline \multirow{3}{*}{Flutter} & \multirow{3}{*}{118,576} & Bug & 13,037 \\  & & Feature & 9967 \\  & & Total & 23,004 \\ \hline \multirow{3}{*}{Roslyn} & \multirow{3}{*}{66,464} & Bug & 12,882 \\  & & Feature & 3824 \\ \cline{1-1}  & & Total & 16,706 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The total number of data per project.  
Each project's issue reports have unique characteristics. In the case of VS Code, the issue reports primarily focus on development environment-related issues, with many reports addressing problems encountered during task execution. For Kubernetes, many issue reports are related to container management and orchestration. Furthermore, there are specific platform-related issues that are reported when running Kubernetes. In Flutter's case, many issue reports pertain to mobile app development, covering topics such as UI, compatibility, and app performance. Lastly, in the case of Roslyn, there is a significant number of issue reports related to C# and VB.NET code analysis. These reports include problems such as static code analysis, code optimization, and the discovery of potential bugs. In this experiment, we collected data from projects with these heterogeneous characteristics.