AI & ML
Robotic-supported Data Loss Detection in Android Applications
arXiv cs.SE0 views
arXiv:2610.20807v1 Announce Type: new Abstract: Smartphones have become integral to modern life due to their diverse applications. However, the development of applications for these devices faces significant challenges, primarily due to two factors: (i) the diverse hardware and operating system versions that require testing across multiple configurations; and (ii) the imperative activity of testing the smartphone in a non-invasive way (similar to an end-user interaction), which is expensive, tedious and error-prone as it is usually carried out manually. In this context, robotic automation has emerged as an effective solution for addressing the diversity of smartphone systems and versions, and in performing some testing tasks non-invasively. In addition, automated robots facilitate quick, accurate, and repetitive testing, thus enabling developers to validate their applications across various configurations effectively. This results in a less invasive verification and in a significant reduction in time spent on manual testing, thus accelerating the development cycle. Our work proposes R-DLD (Robotic-supported Data Loss Detection), a robot-assisted infrastructure for data loss detection in Android applications, offeringless invasive and more realistic tests by interacting directly with smartphone sensors. The robot is constructed using cost-effective materials, facilitating its adoption in testing environments. In our empirical evaluation R-DLD successfully identified 341 data loss issues in 77 randomly selected apps from an Android store. All reported bugs received responses from the developer, with 89.55% confirming the data loss problems, while 35.82% have being subsequently fixed.
Source:https://arxiv.org/abs/2610.20807PreviousHPCAgent-Bench: A Framework for Sound Benchmarking of Code-Optimizing Agents, Harnesses, and SkillsNextSkip the Compressor: Coding Agents Fix More Issues When They Pick Whole Files
Related posts
Most voted crypto projects on Coinvote, Oct 26 to Nov 1, 2026
0dev.to Data
Bluesky This Week — trending topics & AI brand mentions (2026-11-02)
0dev.to Data
Gemini 4 Carbon e DeepSeek V4.1 Flash Cyber: a Guerra Cibernética Autônoma e o Topo do Artificial Analysis
0dev.to Machine Learning
What can an AI agent do for you every day?
0dev.to Machine Learning
Mon modèle de paris semblait excellent sur 3 671 prédictions. Il n'a jamais battu le bookmaker.
0dev.to Machine Learning
HR manager LinkedIn About section examples you can adapt
0dev.to Career