Impact of data quality for automatic issue classification using pre-trained language models

Issue classification aims to recognize whether an issue reports a bug, a request for enhancement or support. In this paper we use pre-trained models for the automatic classification of issues and investigate how the quality of data affects the performance of classifiers. Despite the application of data quality filters, none of our attempts had a significant effect on model quality. As root cause we identify a threat to construct validity underlying the issue labeling.