User interaction-based profiling system for Android application tuning

Lee, Seokjun; Yoon, Chanmin; Cha, Hojung

doi:10.1145/2632048.2636091

Cited by 12 publications

(2 citation statements)

References 16 publications

(17 reference statements)

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“…AppInsight [91] instrumented mobile apps by interposing event handlers to collect information on critical paths that are triggered by user transactions. Lee et al [92] proposed a user interaction-based mobile application profiling system, which analyzes fine-grained information, including user interaction, system behavior, and power consumption, to perform Android app tuning. AndroidPerf [93], a cross-layer profiling system, leverages cross-layer dynamic taint analysis and instrumentation to obtain both the execution information and the performance information about Android apps.…”

Section: Measurement and Monitoring For Appsmentioning

confidence: 99%

Demystifying Application Performance Management Libraries for Android

Tang

Zhan

Zhou

et al. 2019

2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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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.

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Section: Measurement and Monitoring For Appsmentioning

confidence: 99%

Demystifying Application Performance Management Libraries for Android

Tang

Zhan

Zhou

et al. 2019

2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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“…To detect abnormal energy use of mobile apps, researchers use operating system and hardware features as predictors to infer energy information at device, component, virtual machine or application level [15,14,31,17,34,42,28].…”

Section: Detecting and Diagnosing Energy Issuesmentioning

confidence: 99%

Detecting and Diagnosing Energy Issues for Mobile Applications

Yang

Liu

et al. 2019

Preprint

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Energy efficiency is an important criterion to judge the quality of mobile apps, but one third of our randomly sampled apps suffer from energy issues that can quickly drain battery power. To understand these issues, we conducted an empirical study on 27 well-maintained apps such as Chrome and Firefox, whose issue tracking systems are publicly accessible. Our study revealed that the main root causes of energy issues include unnecessary workload and excessively frequent operations. Surprisingly, these issues are beyond the application of present technology on energy issue detection. We also found that 20.6% of energy issues can only manifest themselves under specific contexts such as poor network performance, but such contexts are again neglected by present technology.Therefore, we proposed a novel testing framework for detecting energy issues in real-world apps. Our framework examines apps with well-designed input sequences and runtime contexts. To identify the root causes mentioned above, we employed a machine learning algorithm to cluster the workloads and further evaluate their necessity. For the issues concealed by the specific contexts, we carefully set up several execution contexts to pinpoint them. More importantly, we developed leading edge technology, e.g. pre-designing input sequences with potential energy overuse and tuning tests on-the-fly, to achieve high efficacy in detecting energy issues. A large-scale evaluation shows that 91.6% issues detected in our test were previously unknown to developers. On average, these issues double the energy costs of the apps. Furthermore, our test achieves a low number of false positives. Finally, we show how our test reports can help developers fix the issues.

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User interface-level QoE analysis for Android application tuning

Lee

Cha

2017

Pervasive and Mobile Computing

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User interaction-based profiling system for Android application tuning

Cited by 12 publications

References 16 publications

Demystifying Application Performance Management Libraries for Android

Demystifying Application Performance Management Libraries for Android

Detecting and Diagnosing Energy Issues for Mobile Applications

User interface-level QoE analysis for Android application tuning

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