Context: As mobile applications (apps) widely spread throughout our society and daily life, various personal information is constantly demanded by apps in exchange for more intelligent and customized functionality. An increasing number of users are voicing their privacy concerns through app reviews on app stores. Objective: The main challenge of effectively mining privacy concerns from user reviews lies in that reviews expressing privacy concerns are overridden by a large number of reviews expressing more generic themes and noisy content. In this work, we propose a novel automated approach to overcome that challenge. Method: Our approach first employs information retrieval and document embeddings to extract candidate privacy reviews in an unsupervised manner , which are further labeled to prepare the annotation dataset. Then, supervised classifiers are trained to automatically identify privacy reviews. Finally, an interpretable topic mining algorithm is designed to detect privacy concern topics contained in the privacy reviews. Results: Experimental results show that the best performing document embedding achieves an average precision of 96.80% in the top 100 retrieved candidate privacy reviews, outperforming the taxonomy-based baseline, which achieves 73.87%. All trained privacy review classifiers achieve an F 1 score above 91%, surpassing the keyword-matching baseline by as much as 7.5% and the large language model baseline by up to 2.74%. For detecting privacy concern topics from privacy reviews, our proposed algorithm achieves both better topic coherence and topic diversity than three strong topic modeling baselines, including LDA . Conclusion: Empirical evaluation results demonstrate the effectiveness of our approach in identifying privacy reviews and detecting user privacy concerns in app reviews.

Requirements terminology defines and unifies key specialized and/or technical concepts of the software system, which is significant for understanding the application domain in requirements engineering (RE). However, manual terminology extraction from natural language requirements is laborious and expensive, especially with large scale requirements specifications. In this paper, we aim to employ natural language processing (NLP) techniques and machine learning (ML) algorithms to automatically extract and rank the requirements terms to support high-level feature modeling. To this end, we propose an automatic framework composed of noun phrase identification technique for requirements terms extraction and TextRank combined with semantic similarity for terms ranking. The final ranked terms are organized as a hierarchy, which can be used to help name elements when performing feature modeling. In the quantitative evaluation, our extraction method performs better than three baseline methods in recall with comparable precision. Moreover, our adapted TextRank algorithm can rank more relevant terms at the top positions in terms of average precision compared with most baselines. An illustrative example on the smart home domain further shows the usefulness of our framework in aiding elements naming during feature modeling. The research results suggest that proper adoption and adaption of NLP and ML techniques according to the characteristics of specific RE task could provide automation support for problem domain understanding.