Shohel Ahmed

Shadman Sakib, Oishy Fatema Akhand, Tasnia Tasneem et al.

App store reviews provide a constant flow of real user feedback that can help improve software requirements. However, these reviews are often messy, informal, and difficult to analyze manually at scale. Although automated techniques exist, many do not perform well when replicated and often fail to produce clean, backlog-ready user stories for agile projects. In this study, we evaluate how well large language models (LLMs) such as GPT-3.5 Turbo, Gemini 2.0 Flash, and Mistral 7B Instruct can generate usable user stories directly from raw app reviews. Using the Mini-BAR dataset of 1,000+ health app reviews, we tested zero-shot, one-shot, and two-shot prompting methods. We evaluated the generated user stories using both human judgment (via the RUST framework) and a RoBERTa classifier fine-tuned on UStAI to assess their overall quality. Our results show that LLMs can match or even outperform humans in writing fluent, well-formatted user stories, especially when few-shot prompts are used. However, they still struggle to produce independent and unique user stories, which are essential for building a strong agile backlog. Overall, our findings show how LLMs can reliably turn unstructured app reviews into actionable software requirements, providing developers with clear guidance to turn user feedback into meaningful improvements.

Labiba Farah, Mohammad Ridwan Kabir, Shohel Ahmed et al.

The widespread use of social media applications has raised significant privacy concerns, often highlighted in user reviews. These reviews also provide developers with valuable insights into improving apps by addressing issues and introducing better features. However, the sheer volume and nuanced nature of reviews make manual identification and prioritization of privacy-related concerns challenging for developers. Previous studies have developed software utilities to automatically classify user reviews as privacy-relevant, privacy-irrelevant, bug reports, feature requests, etc., using machine learning. Notably, there is a lack of focus on classifying reviews specifically as privacy-related feature requests, privacy-related bug reports, or privacy-irrelevant. This paper introduces SENtinel SORt (SENSOR), an automated online annotation tool designed to help developers annotate and classify user reviews into these categories. For automating the annotation of such reviews, this paper introduces the annotation model, GRACE (GRU-based Attention with CBOW Embedding), using Gated Recurrent Units (GRU) with Continuous Bag of Words (CBOW) and Attention mechanism. Approximately 16000 user reviews from seven popular social media apps on Google Play Store, including Instagram, Facebook, WhatsApp, Snapchat, X (formerly Twitter), Facebook Lite, and Line were analyzed. Two annotators manually labelled the reviews, achieving a Cohen's Kappa value of 0.87, ensuring a labeled dataset with high inter-rater agreement for training machine learning models. Among the models tested, GRACE demonstrated the best performance (macro F1-score: 0.9434, macro ROC-AUC: 0.9934, and accuracy: 95.10%) despite class imbalance. SENSOR demonstrates significant potential to assist developers with extracting and addressing privacy-related feature requests or bug reports from user reviews, enhancing user privacy and trust.