Proceedings ArticleDOI
Carat: collaborative energy diagnosis for mobile devices
TLDR
During a deployment to a community of more than 500,000 devices, Carat diagnosed thousands of energy anomalies in the wild and increased a user's battery life by 11% after 10 days (compared with 1.9% for the control group).Abstract:
We aim to detect and diagnose energy anomalies, abnormally heavy battery use. This paper describes a collaborative black-box method, and an implementation called Carat, for diagnosing anomalies on mobile devices. A client app sends intermittent, coarse-grained measurements to a server, which correlates higher expected energy use with client properties like the running apps, device model, and operating system. The analysis quantifies the error and confidence associated with a diagnosis, suggests actions the user could take to improve battery life, and projects the amount of improvement. During a deployment to a community of more than 500,000 devices, Carat diagnosed thousands of energy anomalies in the wild. Carat detected all synthetically injected anomalies, produced no known instances of false positives, projected the battery impact of anomalies with 95% accuracy, and, on average, increased a user's battery life by 11% after 10 days (compared with 1.9% for the control group).read more
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Dissertation
Automated analysis of energy efficiency and execution performance for mobile applications
TL;DR: In this article, the authors propose a method to solve the problem of the problem: this article.xii...,.. ] ].. ).. ).
Journal ArticleDOI
Energy Diagnosis of Android Applications: A Thematic Taxonomy and Survey
TL;DR: This article organizes state of the art by surveying 25 relevant studies on Android applications’ automatic energy diagnosis and presents a systematic thematic taxonomy of existing approaches from a software engineering perspective.
Collaborative Mobile Energy Awareness
TL;DR: The author has created a mobile energy measurement application and gathered energy measurement data from over 725,000 devices, running over 300,000 applications, in heterogeneous environments, and constructed models of what is normal in each context for each application, which is the first collaborative approach in the area of mobile energy debugging.
Dissertation
Towards Improving the Quality of Mobile Apps by Leveraging Crowdsourced Feedback
TL;DR: It is claimed that app stores can exploit the wisdom of the crowd to distill actionable insights from the feedback returned by the crowds, which assist app developers to deal with potential errors and threats that affect their apps prior to publication or even when the apps are in the hands of end-users.
Journal ArticleDOI
GreenHub: a large-scale collaborative dataset to battery consumption analysis of android devices
Rui Pereira,Hugo Matalonga,Marco Couto,Fernando Castor,Bruno Cabral,Pedro J. Carvalho,Simão Melo de Sousa,João Paulo Fernandes +7 more
TL;DR: In this article, the authors present the results of a wide analysis of the tendency several smart-phone characteristics have on the battery charge/discharge rate, such as the different models, brands, networks, settings, applications, and even countries.
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