  To provide privacy-aware software systems, it is crucial to consider privacy
from the very beginning of the development. However, developers do not have the
expertise and the knowledge required to embed the legal and social requirements
for data protection into software systems. Objective: We present an approach to
decrease privacy risks during agile software development by automatically
detecting privacy-related information in the context of user story
requirements, a prominent notation in agile Requirement Engineering (RE).
Methods: The proposed approach combines Natural Language Processing (NLP) and
linguistic resources with deep learning algorithms to identify privacy aspects
into User Stories. NLP technologies are used to extract information regarding
the semantic and syntactic structure of the text. This information is then
processed by a pre-trained convolutional neural network, which paved the way
for the implementation of a Transfer Learning technique. We evaluate the
proposed approach by performing an empirical study with a dataset of 1680 user
stories. Results: The experimental results show that deep learning algorithms
allow to obtain better predictions than those achieved with conventional
(shallow) machine learning methods. Moreover, the application of Transfer
Learning allows to considerably improve the accuracy of the predictions, ca.
10%. Conclusions: Our study contributes to encourage software engineering
researchers in considering the opportunities to automate privacy detection in
the early phase of design, by also exploiting transfer learning models.
