Identifying Privacy Concerns in Upcoming Software Release: A Peek into the Future
Hey there, little explorer! 🚀
Imagine you have a super cool new toy car you made! Before you show it to all your friends, you want to make sure it's super safe and fun, right?
This news is about computer games and apps, like the ones on mommy's phone! 📱
Sometimes, these apps ask for things like your name or where you live. That's like asking for your secret treasure map! 🗺️ We want to keep your treasure map safe.
Grown-ups who make apps used to wait until after you played with the app to see if you thought it was safe. But now, clever computer brains have found a way to guess if the app might ask for too much info before you even play with it!
It's like having a magic crystal ball that tells you if your toy car might lose a wheel before you even drive it! 🔮 So, the grown-ups can fix it and make sure your secret treasure map is always safe. Yay! 🎉
arXiv:2604.01393v1 Announce Type: new Abstract: Identifying the features to be released in the next version of software, from a pool of potential candidates, is a challenging problem. User feedback from app stores is frequently used by software vendors for the evolution of apps across releases. Privacy feedback, although smaller in volume, carries a larger impact influencing app's success. Multiple existing work has focused on summarizing privacy concerns at the app level and has also shown that developers utilize feedback to implement security and privacy-related changes in subsequent releases. However, the current literature offers little support for release managers and developers in identifying privacy concerns prior to release. This gap exists as user reviews are typically available i
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Abstract:Identifying the features to be released in the next version of software, from a pool of potential candidates, is a challenging problem. User feedback from app stores is frequently used by software vendors for the evolution of apps across releases. Privacy feedback, although smaller in volume, carries a larger impact influencing app's success. Multiple existing work has focused on summarizing privacy concerns at the app level and has also shown that developers utilize feedback to implement security and privacy-related changes in subsequent releases. However, the current literature offers little support for release managers and developers in identifying privacy concerns prior to release. This gap exists as user reviews are typically available in app stores only after new features of a software system is released. In this paper, we introduce Pre-PI, a novel approach that summarizes privacy concerns for to-be-released features. Our method first maps existing features to semantically similar privacy reviews to learn feature-privacy review relations. We then simulate feedback for candidate features and generate concise summaries of privacy concerns. We evaluate Pre-PI across three real-world apps, and compare it with Hark, a state-of-the-art method that relies on post-release user feedback to identify privacy concerns. Results show that Pre-PI generates additional valid privacy concerns and identifies these concerns earlier than Hark, allowing proactive mitigation prior to release.
Comments: Revising manuscript for IEEE Transactions on Software Engineering
Subjects:
Software Engineering (cs.SE)
Cite as: arXiv:2604.01393 [cs.SE]
(or arXiv:2604.01393v1 [cs.SE] for this version)
https://doi.org/10.48550/arXiv.2604.01393
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Aurek Chattopadhyay [view email] [v1] Wed, 1 Apr 2026 20:51:14 UTC (485 KB)
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