## 6 Discussion
### Discussion on the Text-Code Experiment for VS Code  
As we already discussed in Section 6.1, the MulDIC\({}_{TC}\) model applied to the VS Code project showed no performance improvement. For the VS Code project, the MulDIC\({}_{TC}\) model achieved an F1-score of 67.94%, while the Text Only model achieved a 68.36% F1-score. Therefore, we conjectured and investigated various factors that could be related to the inability of the MulDIC\({}_{TC}\) model.  
First, we investigated the token length of code data, because we set the maximum input length of code data to the value corresponding to each project's third quartile of the  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Project**} & **Mann–Whitney U test** & \multirow{2}{*}{**Effect Size**} \\  & \(\mathbf{p}\)**-Value** & \\ \hline VS Code & 2.2 \(\times\) 10\({}^{-16}\) (\textless\)0.05 & 0.2733451 \\ Kubernetes & 4.73 \(\times\) 10\({}^{-5}\) (\textless\)0.05 & 0.4217934 \\ Flutter & 2.2 \(\times\) 10\({}^{-16}\) (\textless\)0.05 & 0.2554626 \\ Roslyn & 0.159 (\textgreater\)0.05 & 0.1209764 \\ Total & 2.2 \(\times\) 10\({}^{-16}\) (\textless\)0.05 & 0.2564514 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of Mann–Whitney U-test and Effect Size.  
token length of the entire code data. If the input token length is short, the model cuts off substantial data, resulting in significant data loss. On the other hand, if the length is long, the model can create excessively sparse vectors, which can hinder training efficiency. We investigated the statistics of token length for code data across different projects in the form of a boxplot, as shown in Figure 9. A different color represents each project, and the vertical axis represents the length of the tokens. We found that the IQR (InterQuartile Range) of VS Code is the smallest among the projects, so we estimated that VS Code had less data loss than other projects. We concluded that the token length of code data is not the factor that affected the exceptional case.  
Second, when we trained the issue classification models used in our experiments, we assigned the same learning weights to each modality of text, image, and code. This arbitrary weight assignment can be a factor affecting the model performance. For instance, in the case of the VS Code project, if the textual data may be much more informative than the code data, then the Text Only model can achieve a high performance compared to the MulDIC\({}_{TC}\) model. In addition, typically, a multimodal model shares information through interconnections between modalities. If the connectivity among modalities is weak, it can decrease the model performance. Therefore, we conjecture that our weight assignment affected the connectivity among modalities and so the MulDIC\({}_{TC}\) model for the VS Code project had a weak connectivity between the text and code modalities, hindering the synergistic effect.  
Lastly, the difference in the quality of the data can be another factor affecting the model performance. Data quality is a crucial factor affecting the model performance. However, since our experimental projects have issue reports written in different ways by different groups of contributors, the data quality will be different depending on products. Therefore,  
Figure 9: Token length statistics for code data.  
we conjecture that the low-quality nature of the source data in VS Code had a negative effect on the model.