Heterogeneous Graph Learning for Code Reviewers Recommendation with LLM's Understanding

In the era of distributed software development ecosystems featured by dynamic and continuous integration/continuous deployment (CI/CD) workflows, code review process has emerged as a significant quality assurance mechanism, where reviewers systematically inspect and annotate submitted source code to identify potential defects. This practice has gained substantial adoption across both industrial and opensource communities. However, the manual assignment of qualified code reviewers presents significant scalability challenges, particularly in large-scale software projects with exponentially growing code repositories. To overcome this limitation, we propose a novel heterogeneous graph neural network framework enhanced by Large Language Model (LLM) semantic understanding capabilities for automated reviewer recommendation, specifically optimized for code commit contexts.