An Empirical Study on the Performance of Individual Issue Label Prediction

In GitHub, open-source software (OSS) developers label issue reports. As issue labeling is a labor-intensive manual task, automatic approaches have developed to label issue reports. However, those approaches have shown limited performance. Therefore, it is necessary to analyze the performance of predicting labels for an issue report. Understanding labels with high performance and those with low performance can help improve the performance of automatic issue labeling tasks. In this paper, we investigate the performance of individual label prediction. Our investigation uncovers labels with high performance and those with low performance. Our results can help researchers to understand the different characteristics of labels and help developers to develop a unified approach that combines several effective approaches for different kinds of issues.