LabelMate Derives a Project's Own Label Taxonomy From Issue History and Hits 89.84% Labeling Accuracy With No Training Data

arXiv 2609.04055·medium signalXBlueskyLinkedInCopy link

arXiv 2609.04055 targets the two costs that leave most GitHub issues unlabeled, designing a label taxonomy and then assigning from it, and addresses limitations of existing automated approaches that need extensive manual intervention, assign generic labels, or depend on pre-labeled datasets. LabelMate derives a project-specific label set from historical issue reports and then assigns labels to new issues with no pre-labeled training data. Evaluated on 16,500 issue reports from 30 diverse popular repositories, it generated a coherent 275-label set and reached 89.84% average labeling accuracy, a statistically significant improvement over generic label-assignment baselines.