# Detecting Privacy Requirements from User Stories with NLP Transfer Learning Models

arXiv: https://arxiv.org/abs/2202.01035 · HTML: https://mlsanity.com/paper/2202.01035/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 55 references that are in the map.
- First posted on arXiv 2 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Naming the Pain in Requirements Engineering: Contemporary Problems, Causes, and Effects in Practice](https://mlsanity.com/paper/1611.10288/) (2016)
- [Bellwethers: A Baseline Method For Transfer Learning](https://mlsanity.com/paper/1703.06218/) (2017)
- [A Quality Framework for Agile Requirements: A Practitioner's Perspective](https://mlsanity.com/paper/1406.4692/) (2014)
- [An Approach for Reviewing Security-Related Aspects in Agile Requirements Specifications of Web Applications](https://mlsanity.com/paper/1906.11432/) (2019)

## Built on by (most cited first, 1 of 1)

- [Generating Privacy Stories From Software Documentation](https://mlsanity.com/paper/2506.23014/) (2025)

## Abstract (arXiv)

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.