LHGLink: LLM-Enhanced Heterogeneous Graph Learning for Issue–Issue Link Prediction

Abstract

Issue–issue link prediction plays a critical role in supporting software maintenance and evolution by uncovering latent relationships among issues, such as duplicates, dependencies, or related faults. Existing approaches primarily rely on textual similarity or shallow structural features, which often struggle to capture the complex semantic dependencies and rich contextual information inherent in large-scale software repositories. In this study, we propose LHGLink, a novel framework that integrates LLM-based semantic enhancement with heterogeneous graph representation learning for issue-issue link prediction. LHGLink first leverages large language models to refine and enrich noisy and incomplete issue descriptions, and then performs metapath-guided reasoning over a heterogeneous graph that explicitly models issues, assignees, and components. We conduct extensive experiments on 23 large-scale Apache projects collected from Jira. The results show that LHGLink consistently outperforms state-of-the-art baselines across multiple evaluation metrics, achieving an average AUC of 95.11% and an F1-score of 90.79% with statistically significant improvements. Further analyses indicate that heterogeneous node modeling plays a crucial role: component-related information yields stronger gains than assignee information, and their joint modeling improves the F1-score by up to 12.23% over text-only baselines. In addition, LLM-based text enhancement consistently enriches issue semantics and leads to notable performance gains across projects. Comparisons with representative graph-based learning architectures further confirm the superiority and robustness of LHGLink. Overall, this work demonstrates that combining LLM-enhanced semantic understanding with heterogeneous graph modeling provides an effective solution for issue–issue link recovery.