Issue report classification using a multimodal deep learning technique

Abstract

Issue reports are useful resources for developing open-source software and continuously maintaining software products. However, it is not easy to systematically classify the issue reports accumulated hundreds of cases a day. To this end, researchers have studied how to classify issue reports automatically. However, these approaches are limited to applying a text-oriented classification method. In this paper, we apply a multi-modal model-based classification method, which has shown great performance improvement in many fields. We use images attached to an issue report to improve the performance of issue report classification. To evaluate our approach, we conduct an experiment, where we compare the performance of a text-based single-modal model and that of a text and image-based multi-modal model. The experimental results show that the multi-modal method yields 2.1% higher classification f1-score than that of the single-modal method. Based on the experimental results, we will continue our further exploration of the multi-modal model, by considering the characteristics of the issue report and various heterogeneous outputs. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).