## 8 Conclusions  
Issue reports contain textual and other modalities, such as images and code. With this in mind, we proposed MulDIC, a multimodal deep learning model that effectively classifies issue reports using text, image, and code data. To assess the effectiveness of MulDIC and investigate the impact of each modality on the issue report classification task, we conducted experiments. Remarkably, the MulDIC\({}_{TIC}\) model, which utilized text, image, and code data, demonstrated superior performance with 5.07-14.12% improvement in the F1-score, compared to the baseline models. When it comes to the impact of each modality, the image modality with the text modality consistently improved the performance of issue classification tasks. Meanwhile, the code modality mostly improved the performance of issue classification tasks, but not in all of the cases. The combination of all three modalities outperformed any combinations of two modalities. These results indicate that leveraging multimodal approaches considering heterogeneous content types of issue reports can enhance the performance of issue report classification and that the combination of text, image, and code modalities can yield synergistic effects.  
In future research, we plan to continue exploring additional aspects of a multimodal model by considering the characteristics of issue reports and their heterogeneous contents. First, our proposed model is limited in processing each modality using a CNN\(-\)based channel and merging the modality-specific representation vectors through element-wise multiplication. We will explore state-of-the-art multimodal models that can increase the expressive power of each modality and the combination of these text, image, and code modalities. For example, by applying state-of-the-art fusion methods for combining text, image, and code data, we could improve the performance of issue classification tasks. In addition, we could consider adding new modalities or data in future investigations. Second, we applied our proposed model to four different projects, and in one case, a combination of text and code modalities yielded lower performance than that of a Text Only modality. To increase the reliability of our study, we plan to extend our experimental data to include a large number of software projects. Applying our proposed model to more projects will yield more consistent and reliable results.  
Conceptualization, C.K. and S.L.; methodology, C.K.; software, C.K.; validation, C.K. and S.L.; formal analysis, C.K. and S.L.; investigation, S.L.; resources, C.K.; data curation, C.K.; writing--original draft preparation, C.K.; writing--review and editing, C.K., S.L. and P.J.; visualization, C.K.; supervision, S.L. and P.J.; project administration, S.L.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1A2C1094167, RS-2023-00209720). This research was also funded by the \"Leaders in INdustry-university Cooperation 3.0\" Project, supported by the Ministry of Education and National Research Foundation of Korea (LINC3.0-2022-11, 1345356213).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Our issue report datasets are publicly available at [[https://github.com/chang26/MulDIC](https://github.com/chang26/MulDIC)]([https://github.com/chang26/MulDIC](https://github.com/chang26/MulDIC)), accessed on 15 August 2023.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.