  Context: Mobile app reviews written by users on app stores or social media
are significant resources for app developers.Analyzing app reviews have proved
to be useful for many areas of software engineering (e.g., requirement
engineering, testing). Automatic classification of app reviews requires
extensive efforts to manually curate a labeled dataset. When the classification
purpose changes (e.g. identifying bugs versus usability issues or sentiment),
new datasets should be labeled, which prevents the extensibility of the
developed models for new desired classes/tasks in practice. Recent pre-trained
neural language models (PTM) are trained on large corpora in an unsupervised
manner and have found success in solving similar Natural Language Processing
problems. However, the applicability of PTMs is not explored for app review
classification Objective: We investigate the benefits of PTMs for app review
classification compared to the existing models, as well as the transferability
of PTMs in multiple settings. Method: We empirically study the accuracy and
time efficiency of PTMs compared to prior approaches using six datasets from
literature. In addition, we investigate the performance of the PTMs trained on
app reviews (i.e. domain-specific PTMs) . We set up different studies to
evaluate PTMs in multiple settings: binary vs. multi-class classification,
zero-shot classification (when new labels are introduced to the model),
multi-task setting, and classification of reviews from different resources. The
datasets are manually labeled app review datasets from Google Play Store, Apple
App Store, and Twitter data. In all cases, Micro and Macro Precision, Recall,
and F1-scores will be used and we will report the time required for training
and prediction with the models.
