eGEN: an energy-saving modeling language and code generator for location-sensing of mobile apps
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Association for Computing Machinery, Inc
Abstract
Given the limited tool support for energy-saving strategies during the design phase of android applications, developing battery-aware, location-based android applications is a non-trivial task for developers. To this end, we propose eGEN, consisting of (1) a Domain-Specific Modeling Language (DSML) and (2) a code generator to specify and create native battery-aware, location-based mobile applications. We evaluated eGEN by instrumenting the generated battery-aware code in five location-based, open-source android applications and compared the energy consumption with non-eGEN versions. The experimental results show 188 mA (8.34% of battery per hour) of average reduction in battery consumption while showing only 97 meters of degradation in location accuracy over three kilometers of a cycling path. Hence, we see this tool as a first step in helping developers write battery-aware code in location-based android applications. The GitHub repository with source code and all artifacts is available at https://github.com/Kowndinya2000/egen, and the tool demo video at https://youtu.be/Iadfh4cCw8I. © 2022 ACM.
Description
Keywords
code generator, domain-specific language, energy-saving location-sensing, modeling adaptive strategies
Citation
ESEC/FSE 2022 - Proceedings of the 30th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2022, Vol., , p. 1697-1700
