一句话：提出 eGEN，一种面向位置感知移动应用的节能领域建模语言与代码生成器，可生成电池感知原生代码。
针对 Android 应用设计阶段缺乏节能策略工具支持、开发者难以开发电池感知且基于位置的应用这一问题，作者提出 eGEN。eGEN 由两部分组成：领域特定建模语言（DSML）和代码生成器，用于规格化并生成原生、电池感知的、基于位置的移动应用。评估方面，作者将生成的电池感知代码插桩到五个开源位置应用中，并与非 eGEN 版本比较能耗。实验结果显示，平均电池消耗降低 188 mA（每小时约 8.34% 电量），而在三公里骑行路径上定位精度仅退化 97 米。作者认为 eGEN 是帮助开发者在基于位置的 Android 应用中编写电池感知代码的第一步。
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.
DOI 原文 ·
@inproceedings{paperbot7083,
title = {eGEN: an energy-saving modeling language and code generator for location-sensing of mobile apps},
author = {Kowndinya Boyalakuntla and Marimuthu Chinnakali and Sridhar Chimalakonda and Chandrasekaran K},
booktitle = {FSE},
year = {2022},
doi = {10.1145/3540250.3558914}
}