  App reviews reflect various user requirements that can aid in planning
maintenance tasks. Recently, proposed approaches for automatically classifying
user reviews rely on machine learning algorithms. A previous study demonstrated
that models trained on existing labeled datasets exhibit poor performance when
predicting new ones. Therefore, a comprehensive labeled dataset is essential to
train a more precise model. In this paper, we propose a novel approach that
assists in augmenting labeled datasets by utilizing information extracted from
an additional source, GitHub issues, that contains valuable information about
user requirements. First, we identify issues concerning review intentions (bug
reports, feature requests, and others) by examining the issue labels. Then, we
analyze issue bodies and define 19 language patterns for extracting targeted
information. Finally, we augment the manually labeled review dataset with a
subset of processed issues through the Within-App, Within-Context, and
Between-App Analysis methods. We conducted several experiments to evaluate the
proposed approach. Our results demonstrate that using labeled issues for data
augmentation can improve the F1-score to 6.3 in bug reports and 7.2 in feature
requests. Furthermore, we identify an effective range of 0.3 to 0.7 for the
auxiliary volume, which provides better performance improvements.
