Energy inefficiency diagnosis for Android applications: a literature review  

作　　者：Yuxia SUN Jiefeng FANG Yanjia CHEN Yepang LIU Zhao CHEN Song GUO Xinkai CHEN Ziyuan TAN 

机构地区：[1]Department of Computer Science,Jinan University,Guangzhou 510632,China [2]Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen 518055,China [3]The Department of Computing,Hong Kong Polytechnic University,Hong Kong 999077,China

出　　处：《Frontiers of Computer Science》2023年第1期35-50,共16页中国计算机科学前沿（英文版）

基　　金：supported by the Guangdong Basic and Applied Basic Research Foundation(2021A1515012297);the Shenzhen Science and Technology Innovation Commission(R2020A045);the Open Project of Guangdong Provincial Key Laboratory of High-Performance Computing(2021).

摘　　要：Android applications are becoming increasingly powerful in recent years. While their functionality is still of paramount importance to users, the energy efficiency of these applications is also gaining more and more attention. Researchers have discovered various types of energy defects in Android applications, which could quickly drain the battery power of mobile devices. Such defects not only cause inconvenience to users, but also frustrate Android developers as diagnosing the energy inefficiency of a software product is a non-trivial task. In this work, we perform a literature review to understand the state of the art of energy inefficiency diagnosis for Android applications. We identified 55 research papers published in recent years and classified existing studies from four different perspectives, including power estimation method, hardware component, types of energy defects, and program analysis approach. We also did a cross-perspective analysis to summarize and compare our studied techniques. We hope that our review can help structure and unify the literature and shed light on future research, as well as drawing developers' attention to build energy-efficient Android applications.

关 键 词：Android applications energy defects energy inefficiency diagnosis 

分 类 号：TP393.09[自动化与计算机技术—计算机应用技术]