  Studies show that developers' answers to the mobile app users' feedbacks on
app stores can increase the apps' star rating. To help app developers generate
answers that are related to the users' issues, recent studies develop models to
generate the answers automatically. Aims: The app response generation models
use deep neural networks and require training data. Pre-Trained neural language
Models (PTM) used in Natural Language Processing (NLP) take advantage of the
information they learned from a large corpora in an unsupervised manner, and
can reduce the amount of required training data. In this paper, we evaluate
PTMs to generate replies to the mobile app user feedbacks. Method: We train a
Transformer model from scratch and fine-tune two PTMs to evaluate the generated
responses, which are compared to RRGEN, a current app response model. We also
evaluate the models with different portions of the training data. Results: The
results on a large dataset evaluated by automatic metrics show that PTMs obtain
lower scores than the baselines. However, our human evaluation confirms that
PTMs can generate more relevant and meaningful responses to the posted
feedbacks. Moreover, the performance of PTMs has less drop compared to other
models when the amount of training data is reduced to 1/3. Conclusion: PTMs are
useful in generating responses to app reviews and are more robust models to the
amount of training data provided. However, the prediction time is 19X than
RRGEN. This study can provide new avenues for research in adapting the PTMs for
analyzing mobile app user feedbacks. Index Terms-mobile app user feedback
analysis, neural pre-trained language models, automatic answer generation
