Studying Permission Related Issues in Android Wearable Apps
Bibliographic record
Abstract
Wearable devices are becoming increasingly popular; these devices host software that is known as wearable apps. Wearable apps could be packaged alongside handheld apps, hence they must be installed on the accompanying device (e.g., smartphone). This device dependency causes both apps to be also tightly coupled. Most importantly, when a wearable app is distributed by embedded it in a handheld app, Android Wear platform requires to include the wearable permission also in the handheld app which is error-prone. In this paper, we defined two permission issues related to wearable apps-namely permission mismatches and superfluous features. To study the permission related issues, we propose a technique to detect permission issues in wearable apps. We implement our technique in a tool called Permlyzer, which automatically detects these permission issues from an app's APK. We run Permlyzer on a dataset of 2,724 apps that have embedded wearable version and 339 standalone wearable app. Our result shows that I) 6% of wearable apps that request permissions are suffering from the permission mismatching problem; II) out of the apps that requires underlying features, 523 (52.4%) of handheld apps and 66 (80.5%) of standalone wearable apps have at least one superfluous feature; III) all the studied apps missed a declaration of underlying features for one or more of their permissions, which shows that developers may not know the mapping between the permissions they request and the hardware features. Additionally, in a survey of wearable app developers, all of the developers that responded mention that having a tool like Permlyzer, that detect permission related issues would be useful to them. Our results contribute to the understanding of permissions related issues in wearable apps, in particular, proposing a technique to detect permission mismatch and superfluous features.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".