## 1 Introduction  
For software maintenance, software developers utilize issue-tracking systems to quickly incorporate user requirements into their software products. While using the software products, users report bugs, feature suggestions, and other comments by creating issue reports. Developers can refer to the issue reports to make changes to the software products. However, the number of issue reports rapidly increases. In the case of active open-source software projects, hundreds of issue reports are accumulated daily. Manually managing such issue reports requires a significant amount of effort from developers.  
Researchers have conducted studies on automatically classifying issue reports to systematically manage issue reports [1; 2; 3; 4; 5; 6; 7; 8; 9]. Some of them [1; 2; 3; 6; 7; 9] focused on classifying issue reports into a few categories, such as bug or non-bug. For example, [PERSON] et al. [7] employed FastText to classify issue reports into three categories: Bug, Enhancement, and Question. Other studies attempt to classify issue reports into more categories [5; 8; 10]. However, all of those studies solely utilized a single modality, thereby missing out on the potential benefits of incorporating heterogeneous information from issue reports. As a result, those models struggled to attain a comprehensive understanding of the issue report due to the limitations imposed by the scarcity of information. Consequently, such a single modality hinders the potential for substantial enhancements in issue classification performance.  
In this paper, we notice that issue reports contain various types of information, including textual titles, body contents, images, videos, and code. Figure 1 demonstrates an issue report that contains text, code, and images together. This is an example of an issue report in VS Code with issue number #147154. We expect that not only text data but also image and code data of issue reports can contribute to the performance of the issue classification, because the additional data can elaborate the representations of issue reports. Therefore, we propose MulDIC, a multimodal deep learning-based issue classification model that uses text, image, and code data of issue reports.  
To evaluate the proposed model MulDIC, we conducted experiments to examine whether the proposed multimodal model, MulDIC, which combines text, image, and code data can improve the performance of issue classification. For the experiments, we selected four projects with many issue reports: VS Code, Kubernetes, Flutter, and Roslyn. With the four projects, we compared the performance of our proposed multimodal model with that of the state-of-the-art unimodal model proposed by [PERSON] et al. (2010). Our experimental results showed that our proposed approach achieved a higher F1-score by 5.07% to 14.12% compared to the existing method, improving the issue report classification performance in all projects.  
The contributions of our study are as follows:  
1. We propose the first multimodal model that combines text, image, and code modalities for issue classification tasks.
2. We evaluate the effectiveness of our proposed multimodal model by comparing a text-based unimodal model in issue classification tasks.
3. We analyze the effect of image and code modality by experimenting with a combination of several different modalities.  
Figure 1: Example of an issue report in VS Code.  
We make the code and datasets we used for our experiment available on GitHub.  
The remainder of this paper is organized as follows. Section 2 introduces the related works. Section 3 proposes our approach. Section 4 describes the experimental setup. Section 5 presents the experimental results and discusses the results. Section 6 concludes the paper.