Android apps continuously evolve to meet user expectations and thrive in the competitive environment of app stores. Hence, making informed decisions is crucial for the success of upcoming releases. In recent years, researchers have sought to aid developers in release planning by studying, analyzing, and modeling evolutionary information derived from tracking releases of various apps. They have demonstrated how the types of information provided on different platforms can effectively predict and assess the quality or success of an app. Nevertheless, this field needs a comprehensive dataset containing evolutionary information that tracks various releases of open-source Android apps. Existing datasets lack release-level information, are not publicly available, or do not cover a wide range of up-to-date apps. This paper introduces a dataset comprising diverse metadata adapted from GitHub and Google Play, along with impactful metrics extracted from the source code of 8,041 published releases of 1,363 open-source Android apps.

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.