The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
PUMA: programmable UI-automation for large-scale dynamic analysis of mobile apps
Abstract
Mobile app ecosystems have experienced tremendous growth in the last six years. This has triggered research on dynamic analysis of performance, security, and correctness properties of the mobile apps in the ecosystem. Exploration of app execution using automated UI actions has emerged as an important tool for this research. However, existing research has largely developed analysis-specific UI automation techniques, wherein the logic for exploring app execution is intertwined with the logic for analyzing app properties. PUMA is a programmable framework that separates these two concerns. It contains a generic UI automation capability (often called a Monkey) that exposes high-level events for which users can define handlers. These handlers can flexibly direct the Monkey's exploration, and also specify app instrumentation for collecting dynamic state information or for triggering changes in the environment during app execution. Targeted towards operators of app marketplaces, PUMA incorporates mechanisms for scaling dynamic analysis to thousands of apps. We demonstrate the capabilities of PUMA by analyzing seven distinct performance, security, and correctness properties for 3,600 apps downloaded from the Google Play store.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Android permissions: user attention, comprehension, and behavior
Felt, A. P., Ha, E., Egelman, S., Haney, A., Chin, E., & Wagner, D. (2012) · Proceedings of the Eighth Symposium on Usable Privacy and Security · doi:10.1145/2335356.2335360 · Cited by 1,022
Cited by
A Multimodal Deep Learning Method for Android Malware Detection Using Various Features
Kim, T., Kang, B., Rho, M., Sezer, S., & Im, E. G. (2019) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2866319 · Cited by 518
A Survey of App Store Analysis for Software Engineering
Martin, W., Sarro, F., Jia, Y., Zhang, Y., & Harman, M. (2017) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2630689 · Cited by 459
ReCon: Revealing and Controlling PII Leaks in Mobile Network Traffic
Ren, J., Rao, A., Lindorfer, M., Legout, A., & Choffnes, D. (2016) · Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services · doi:10.1145/2906388.2906392 · Cited by 132
Examining Image-Based Button Labeling for Accessibility in Android Apps through Large-Scale Analysis
Ross, A. S., Zhang, X., Fogarty, J., & Wobbrock, J. O. (2018) · Proceedings of the 20th International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3234695.3236364 · Cited by 90
(2024) · doi:10.1145/3613904.3642939 · Cited by 31
Metadata
- Authors
- Hao, S., Liu, B., Nath, S., Halfond, W. G. J., & Govindan, R.
- Venue
- Proceedings of the 12th annual international conference on Mobile systems, applications, and services
- Publisher
- ACM
- Published
- 2014-06-02
- Pages
- 204-217
- OpenAlex
- W2101800210
- Cited by
- 340
- References
- 1
- FWCI
- 30.77
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: PUMA: programmable UI-automation for large-scale dynamic analysis of mobile apps. https://app.cochat.ai/papers/10.1145/2594368.2594390
- The paper
- Hao, S., Liu, B., Nath, S., Halfond, W. G. J., & Govindan, R. (2014). PUMA: programmable UI-automation for large-scale dynamic analysis of mobile apps. Proceedings of the 12th annual international conference on Mobile systems, applications, and services. https://doi.org/10.1145/2594368.2594390
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
