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Context: Mobile app reviews written by users on app stores or social mediaare significant resources for app developers.Analyzing app reviews have provedto be useful for many areas of software engineering (e.g., requirementengineering, testing). Automatic classification of app reviews requiresextensive efforts to manually curate a labeled dataset. When the classificationpurpose changes (e.g. identifying bugs versus usability issues or sentiment),new datasets should be labeled, which prevents the extensibility of thedeveloped models for new desired classes/tasks in practice. Recent pre-trainedneural language models (PTM) are trained on large corpora in an unsupervisedmanner and have found success in solving similar Natural Language Processingproblems. However, the applicability of PTMs is not explored for app reviewclassification Objective: We investigate the benefits of PTMs for app reviewclassification compared to the existing models, as well as the transferabilityof PTMs in multiple settings. Method: We empirically study the accuracy andtime efficiency of PTMs compared to prior approaches using six datasets fromliterature. In addition, we investigate the performance of the PTMs trained onapp reviews (i.e. domain-specific PTMs) . We set up different studies toevaluate 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. Thedatasets are manually labeled app review datasets from Google Play Store, AppleApp 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 trainingand prediction with the models.

Evaluating Pre-Trained Models for User Feedback Analysis in Software Engineering: A Study on Classification of App-Reviews