Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA Systematical Study on Application Performance Management Libraries for Apps
65
0
0.0
(
0
)
Added by
Yutian Tang
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
Being able to automatically detect the performance issues in apps can significantly improve apps quality as well as having a positive influence on user satisfaction. Application Performance Management (APM) libraries are used to locate the apps performance bottleneck, monitor their behaviors at runtime, and identify potential security risks. Although app developers have been exploiting application performance management (APM) tools to capture these potential performance issues, most of them do not fully understand the internals of these APM tools and the effect on their apps. To fill this gap, in this paper, we conduct the first systematic study on APMs for apps by scrutinizing 25 widely-used APMs for Android apps and develop a framework named APMHunter for exploring the usage of APMs in Android apps. Using APMHunter, we conduct a large-scale empirical study on 500,000 Android apps to explore the usage patterns of APMs and discover the potential misuses of APMs. We obtain two major findings: 1) some APMs still employ deprecated permissions and approaches, which makes APMs fail to perform as expected; 2) inappropriate use of APMs can cause privacy leaks. Thus, our study suggests that both APM vendors and developers should design and use APMs scrupulously.
rate research
Read More
This paper presents the application of a meta model and single underlying model on an applied avionics system design use case. System models, safety assurance cases and safety requirements are maintained in a central repository. This enables to link these data which are originally developed in unrelated tools. By having such a central repository, traceability can be established, and consistency can be ensured, which leads to less errors and a shorter development time. A meta model was constructed which matches the central repository to enable bidirectional synchronization with an external authoring tool.
The process of developing a mobile application typically starts with the ideation and conceptualization of its user interface. This concept is then translated into a set of mock-ups to help determine how well the user interface embodies the intended features of the app. After the creation of mock-ups developers then translate it into an app that runs in a mobile device. In this paper we propose an approach, called GUIGLE, that aims to facilitate the process of conceptualizing the user interface of an app through GUI search. GUIGLE indexes GUI images and metadata extracted using automated dynamic analysis on a large corpus of apps extracted from Google Play. To perform a search, our approach uses information from text displayed on a screen, user interface components, the app name, and screen color palettes to retrieve relevant screens given a query. Furthermore, we provide a lightweight query language that allows for intuitive search of screens. We evaluate GUIGLE with real users and found that, on average, 68.8% of returned screens were relevant to the specified query. Additionally, users found the various different features of GUIGLE useful, indicating that our search engine provides an intuitive user experience. Finally, users agree that the information presented by GUIGLE is useful in conceptualizing the design of new screens for applications.
Third-party libraries (TPLs) have been widely used in mobile apps, which play an essential part in the entire Android ecosystem. However, TPL is a double-edged sword. On the one hand, it can ease the development of mobile apps. On the other hand, it also brings security risks such as privacy leaks or increased attack surfaces (e.g., by introducing over-privileged permissions) to mobile apps. Although there are already many studies for characterizing third-party libraries, including automated detection, security and privacy analysis of TPLs, TPL attributes analysis, etc., what strikes us odd is that there is no systematic study to summarize those studies endeavors. To this end, we conduct the first systematic literature review on Android TPL-related research. Following a well-defined systematic literature review protocol, we collected 74 primary research papers closely related to the Android third-party library from 2012 to 2020. After carefully examining these studies, we designed a taxonomy of TPL-related research studies and conducted a systematic study to summarize current solutions, limitations, challenges and possible implications of new research directions related to third-party library analysis. We hope that these contributions can give readers a clear overview of existing TPL-related studies and inspire them to go beyond the current status quo by advancing the discipline with innovative approaches.
Power efficiency is critical in high performance computing (HPC) systems. To achieve high power efficiency on application level, it is vital importance to efficiently distribute power used by application checkpoints. In this study, we analyze the relation of application checkpoints and their power consumption. The observations could guide the design of power management.
We demonstrate a specific method and technology for model-based testing of large software projects with the QuickCheck tool using property-based specifications. Our specifications are very precise, state-full models of the software under test (SUT). In our approach we define (a) formal descriptions of valid function call sequences (public API), (b) postconditions that check the validity of each call, and (c) call-out specifications that define and validate external system interactions (SUT calling external API). The QuickCheck tool automatically generates and executes tests from these specifications. Commercially, this method and tool have been used to test large parts of the industrially developed automotive libraries based on the Autosar standard. In this paper, we exemplify our approach with a circular buffer specified by Autosar, to demonstrate the capabilities of the model-based testing method of QuickCheck. Our example is small compared to the commercial QuickCheck models, but faithfully addresses many of the same challenges.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
