TY - GEN
T1 - MLStar
T2 - 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Mobiquitous 2018
AU - Gaska, Benjamin
AU - Gniady, Chris
AU - Surdeanu, Mihai
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/11/5
Y1 - 2018/11/5
N2 - Improving the energy efficiency of smartphones is critical for increasing the utility that they provide to the users. With most mobile operating systems, users are responsible for managing their phone’s battery efficiency by utilizing the various settings provided by the operating system, as well as selecting energy-efficient apps. However, current app marketplaces do not provide users with information about app energy efficiency, which makes it challenging for the user to make informed decision when selecting an app. This paper presents a novel machine learning approach to estimate app energy efficiency by utilizing textual information available in the Google Play store such as an app’s description, user reviews, as well as system permissions. Our detailed analysis of the resulting system shows that hardware permissions, app description, and user reviews correlate well with energy efficiency ratings. We evaluate five models that represent popular classes of machine learning algorithms in their ability to predict energy efficiency ratings. Finally, we compare our approach to gold truth ratings obtained by the actual energy profiling of the app, demonstrating that the proposed system is able to estimate an app’s energy efficiency within less than 1 point on the 1 – 5 scale provided by the profiler, without requiring any kind of profiling.
AB - Improving the energy efficiency of smartphones is critical for increasing the utility that they provide to the users. With most mobile operating systems, users are responsible for managing their phone’s battery efficiency by utilizing the various settings provided by the operating system, as well as selecting energy-efficient apps. However, current app marketplaces do not provide users with information about app energy efficiency, which makes it challenging for the user to make informed decision when selecting an app. This paper presents a novel machine learning approach to estimate app energy efficiency by utilizing textual information available in the Google Play store such as an app’s description, user reviews, as well as system permissions. Our detailed analysis of the resulting system shows that hardware permissions, app description, and user reviews correlate well with energy efficiency ratings. We evaluate five models that represent popular classes of machine learning algorithms in their ability to predict energy efficiency ratings. Finally, we compare our approach to gold truth ratings obtained by the actual energy profiling of the app, demonstrating that the proposed system is able to estimate an app’s energy efficiency within less than 1 point on the 1 – 5 scale provided by the profiler, without requiring any kind of profiling.
KW - Energy
KW - Machine learning
KW - Mobile
UR - http://www.scopus.com/inward/record.url?scp=85060004462&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85060004462&partnerID=8YFLogxK
U2 - 10.1145/3286978.3287011
DO - 10.1145/3286978.3287011
M3 - Conference contribution
AN - SCOPUS:85060004462
T3 - ACM International Conference Proceeding Series
SP - 216
EP - 225
BT - Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems
PB - Association for Computing Machinery
Y2 - 5 November 2018 through 7 November 2018
ER -