ScholarWorks@경상국립대학교

초록

Software continuously evolves through changes, and issue reports encapsulate these change requests. In the GitHub system, a labeling mechanism has been introduced for systematic issue management, but significant effort from developers is required to label and manage these issues. To address this, numerous attempts have been made in previous research to automate issue report classification. However, these attempts have shown limitations in classification accuracy. We experiment to determine if integrating heterogeneous information through a multimodal model that combines text, images, and code from issue reports can improve classification accuracy. Specifically, we investigate whether training the model on extensive issue data can enhance classification accuracy. Experimental results show that the multimodal approach outperforms single-modal models by 5.50-7.01% in terms of F1-Score, demonstrating superior performance. These findings indicate that leveraging heterogeneous data sources in issue reports is effective in improving classification performance.

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