GlobalTagNet: A Graph-Based Framework for Multi-Label Classification in GitHub Issues

Issue reports in software repositories serve as a vital channel for users to report problems, bugs, and provide valuable suggestions for project improvements. Issues are typically assigned a diverse range of labels, enabling categorization and organization. However, the manual labeling of these reports becomes laborious and challenging due to the sheer volume of projects and issue reports. Existing automatic labeling solutions often rely on text matching or multi-classification approaches, which may overlook label dependencies and contextual correlations, compromising the accuracy and relevance of issue classification. To address this challenge, we propose an innovative framework named GlobalTagNet that enhances the automation of issue label assignment. Our approach leverages the power of Heterogeneous Graph Transformer (HGT) to explore label dependencies and semantics. By utilizing hierarchical graph structures, our model captures global label dependencies and correlations, enabling effective learning of label relationships from a holistic perspective. Furthermore, our approach enables the integration of information across multiple levels of the graph, facilitating more accurate inference of label relationships. We conducted comprehensive experiments to assess the effectiveness of our proposed approach. The experimental results unequivocally demonstrate that our method surpasses the performance of baseline solutions, achieving a remarkable improvement of up to 6% in Accuracy, and 8% in F1-score.