Battery-aware transformations in mobile applications

Cito, Jürgen; Rubin, Julia; Stanley-Marbell, Phillip; Rinard, Martin

doi:10.1145/2970276.2970324

Cited by 10 publications

(3 citation statements)

References 13 publications

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“…Source code adaptation approaches can also focus on particular elements of devices, such as the display. For instance, energy consumption of displays has Pathak et al [146] Fine-grained energy-profling Yan et al [90] App preload Banerjee and Roychoudhury [182] Automatic Refactoring Liu et al [13] Antipatterns Fig. 4.…”

Section: Discussionmentioning

confidence: 99%

“…Zhang et al [25] measured power consumption using battery voltage sensors and knowledge of battery discharge behavior. Additionally, fuel gauge chips [146] and Battery Monitoring Unit [147] can be used to measure energy consumption. Other approaches use physical power meters to measure energy consumption [99], [148], [149], [150], [151].…”

Section: Profilingmentioning

confidence: 99%

“…Another approach to automatically refactor applications is to follow energy efficiency guidelines [190], [191], [194]. Cito et al [146] adapted application binaries to adjust the frequency of network requests to advertisements and analytics based on the battery status. Anwer et al [186] adapted permissions and corresponding source code of applications based on user requirements, which can for example prevent the unconscious sending of an SMS.…”

Section: Optimization Approachesmentioning

confidence: 99%

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A Survey of Performance Optimization for Mobile Applications

Hort

Kechagia

Sarro

et al. 2022

IIEEE Trans. Software Eng.

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To ensure user satisfaction and success of mobile applications, it is important to provide highly performant applications. This is particularly important for resource-constrained systems such as mobile devices. Thereby, non-functional performance characteristics, such as energy and memory consumption, play an important role for user satisfaction. This paper provides a comprehensive survey of non-functional performance optimization for Android applications. We collected 156 unique publications, published between 2008 and 2020, that focus on the optimization of performance of mobile applications. We target our search at four performance characteristics: responsiveness, launch time, memory and energy consumption. For each performance characteristic, we categorize optimization approaches based on the method used in the corresponding publications. Furthermore, we identify research gaps in the literature for future work.

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Section: Discussionmentioning

confidence: 99%