PUMA: programmable UI-automation for large-scale dynamic analysis of mobile apps

By Shuai Hao, Bin Liu, Suman Nath, William G. J. Halfond, Ramesh Govindan. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2014

372 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/2166ed56495a8e528f891067a138f63913e9fb00

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Koushik Sen
2. George C. Necula
3. Wontae Choi
4. Sakir Sezer
5. Yue Jia
6. Mark Harman
7. Yao Guo
8. Xiangqun Chen

In Information Systems

1. Yue Jia
2. Koushik Sen
3. George C. Necula
4. Wontae Choi
5. Mark Harman
6. Yuanyuan Zhang
7. Federica Sarro
8. William J. Martin

In Signal Processing

1. Mark Harman
2. Yue Jia
3. Koushik Sen
4. George C. Necula
5. Wontae Choi
6. Yuting Chen
7. Ting Su
8. Zhendong Su

Citations in the corpus

• A Survey of App Store Analysis for Software Engineering (2017, background): “[91], 2014 MobiSys 3,600 Dering and McDaniel [57], 2014 MILCOM 450,000 Linares-V asquez et al.”
• DroidBot: A Lightweight UI-Guided Test Input Generator for Android (2017, methodology): “PUMA [10] is a model-based test framework, which is programmable with PUMAScript.”
• Guided GUI testing of android apps with minimal restart and approximate learning (2013, background): “Their results suggest that Monkey outperforms more sophisticated tools [8,13,23,47,69,117] in terms of maximizing coverage in a limited period of time.”
• Brahmastra: Driving Apps to Test the Security of Third-Party Components (2014, methodology): “We began our research by exploring automated runtime analysis tools that drive mobile UIs (e.g., [5, 23, 26]) to exercise the third-party component, but quickly found this approach to be insufficient.”
• *droid (2016, background): “We refer readers to that work for more information on Android test generation [Amalfitano et al. 2012; Aviv et al. 2012; Xu et al. 2012; Gomez et al. 2013; Machiry et al. 2013; Hao et al. 2014; Liang et al. 2014; Mahmood et al. 2014; Narain et al. 2014].”
• Guided, stochastic model-based GUI testing of Android apps (2017, methodology): “In detail, MobiGUITAR determines the equivalence of app states on the basis of the properties (ids and types) of their constitutive UI objects, while PUMA differentiates states according to their UI features (e.g., the number of invocable events).”
• Free for All! Assessing User Data Exposure to Advertising Libraries on Android (2016, background): “For example, PUMA [21] is a very promising dynamic analysis tool introduced recently.”
• A Multimodal Deep Learning Method for Android Malware Detection Using Various Features (2019, methodology): “The malware detection approaches can be classified into two categories; static analysis based detection [2-19] and dynamic analysis based detection [20-24].”