Abstract: This paper focuses on the GitHub Issue-PR link prediction task,with the main contributions as follows.Firstly,a framework for collecting real-world Issue-PR link datasets based on open-source collaboration is constructed.Built on the IPCrawler framework,it efficiently collects open-source collaboration data by integrating GitHub API and GH Archive data.Na-tural language processing methods are used to parse potential links,and a fine-tuned pre-trained language model is introduced for semantic understanding and classification,constructing a high-quality benchmark dataset.The accuracy improves by 8.05 percen-tage points compared with the rule-based method.Secondly,a GitHub Issue-PR link prediction model named HGTS-IPR(Heterogeneous Graph and Text Sequence for Issue-Pull Request Linking) is designed,which integrates heterogeneous graph learning and text sequence learning.A heterogeneous information network is constructed,and a heterogeneous graph neural network based on meta-paths and graph attention mechanisms is adopted for feature extraction,while text sequence learning is incorporated to enhance semantic modeling.A multi-task learning framework is used for joint optimization.On five mainstream open-source project datasets,the model generally outperforms the baseline methods in terms of the AP and AUC metrics.

Key words: GitHub, Issue-PR linking prediction, Heterogeneous graph neural network, Text sequence modeling, Multi-task lear-ning