Personalizing label prediction for GitHub issues

Abstract

Automated label prediction tools can help developers manage and categorize issues on GitHub. However, different open-source projects use various forms of labels with the same meaning. Previous label prediction methods mainly solve the problem of the synonymous labels by manual preprocessing rules, but these preprocessing rules can only identify synonyms with the same prefix or suffix. These factors inspire us to propose a method to identify these synonymous labels automatically and recommend personalized labels for different open-source projects. In this paper, we propose a Personalizing Label Prediction framework for Issues named PLPI. PLPI identifies labels with similar meanings by representing labels as semantic vectors and applying clustering methods . PLPI can predict personalized labels from the existing labels in the open-source project. We conduct a comprehensive study to compare seven commonly adopted labeling models with our approach. The experimental results demonstrate the advantages of our approach. Finally, we show some representative examples and discuss the visualization results of synonyms clustering by dimension reduction. The experimental results show that our method PLPI can improve label prediction performance and provide personalized label recommendation results for different open-source projects.