However, unethical authors tend to develop malware in the devices for wealth, fame, or private purposes.
#Stack the states 1 vs 2 android#
Notably, the employment of OS leads to a rapid increase in the number of Android users. Therefore, many manufacturers are utilizing this OS to produce mobile devices (smartphones, smartwatch, and smart glasses) in different brands, including Google Pixel, Motorola, Samsung, and Sony.
#Stack the states 1 vs 2 free#
Researchers and practitioners can use the results of this study to (i) identify existing research/technical gaps to target, (ii) understand how approaches developed in academia can be successfully transferred to industry, and (iii) better position their (past and future) approaches for static analysis of mobile apps.Īndroid is a free open-source operating system (OS), which allows an in-depth understanding of its architecture. The results of this study give a solid foundation for assessing existing and future approaches for static analysis of mobile apps, especially in terms of their industrial adoptability. We analyzed each primary study according to a rigorously-defined classification framework.
Starting from over 12,000 potentially relevant studies, we applied a rigorous selection procedure resulting in 261 primary studies along a time span of 9 years. In this paper, we present a systematic mapping study aimed at identifying, evaluating and classifying characteristics, trends and potential for industrial adoption of existing research in static analysis of mobile apps.
Over the years, literally hundreds of static analysis techniques have been proposed, ranging from structural and control-flow analysis to state-based analysis. Static analysis is gaining a growing interest, allowing developers to predict properties about the run-time behavior of mobile apps without executing them. Mobile platforms are rapidly and continuously changing, with support for new sensors, APIs, and programming abstractions. The evaluations on 35 commonly used apps validate the high-precision of the generated activity launching graphs, and indicate that the proposed back-stack transition analysis outperforms the state-of-the-art one by 29.8% in avoiding 3-length infeasible paths. The study conducted on 968 open source apps from F-droid indicates that non-default configurations are in common use. The proposed analysis contains two types of graph constructions: the activity launching graph that represents activity transitions with extracted back-stack-related configurations, and the back-stack transition graph that simulates the changes of the back stack triggered by activity launchings and back events. In this paper, we present a tool, BSSimulator, to perform the static back-stack transition analysis. Existing activity transition analyses are not sensitive to back-stack-related configurations, which suppose all activities are launched with the default configuration, and thus model infeasible transitions and incorrect back-stack states when there are non-default configurations in an application. Therefore, back-stack transition analysis is closely related to activity transition analysis, which is fundamental for understanding Android behaviors. Users’ interactions may trigger activity launchings, which change the state of the back stack. In Android, activity instances at an application’s run time are organized in a back stack, and users always interact with the top instance of the stack.
0 kommentar(er)
