Detecting and Diagnosing Energy Issues for Mobile Applications

Xueliang Li, Yuming Yang, Yepang Liu, John Patrick Gallagher, Kaishun Wu

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Energy efficiency is an important criterion to judge the quality of mobile apps, but one third of our randomly sampled apps suffer from energy issues that can quickly drain battery power. To understand these issues, we conducted an empirical study on 27 well-maintained apps such as Chrome and Firefox, whose issue tracking systems are publicly accessible. Our study revealed that the main root causes of energy issues include unnecessary workload and excessively frequent operations. Surprisingly, these issues are beyond the application of present technology on energy issue detection. We also found that 25.0% of energy issues can only manifest themselves under specific contexts such as poor network performance, but such contexts are again neglected by present technology. In this paper, we propose a novel testing framework for detecting energy issues in real-world mobile apps. Our framework examines apps with well-designed input sequences and runtime contexts. To identify the root causes mentioned above, we employed a machine learning algorithm to cluster the workloads and further evaluate their necessity. For the issues concealed by the specific contexts, we carefully set up several execution contexts to catch them. More importantly, we designed leading edge technology, e.g. pre-designing input sequences with potential energy overuse and tuning tests on-the-fly, to achieve high efficacy in detecting energy issues. A large-scale evaluation shows that 91.6% issues detected in our experiments were previously unknown to developers. On average, these issues double the energy costs of the apps. Our testing technique achieves a low number of false positives.
Original languageEnglish
Title of host publication29th ACM SIGSOFT International Symposium on Software Testing and Analysis : ISSTA '20
EditorsSarfraz Khurshid, Corina S. Pasareanu
PublisherAssociation for Computing Machinery
Publication date2020
Pages115-127
ISBN (Electronic)9781450380089
DOIs
Publication statusPublished - 2020
Event29th ACM SIGSOFT International Symposium on Software Testing and Analysis - Online, Los Angeles, United States
Duration: 18 Jul 202022 Jul 2020
Conference number: 29
https://conf.researchr.org/home/issta-2020

Conference

Conference29th ACM SIGSOFT International Symposium on Software Testing and Analysis
Number29
LocationOnline
CountryUnited States
CityLos Angeles
Period18/07/202022/07/2020
OtherVirtual conference
Internet address

Keywords

  • Program analysis
  • energy analysis

Cite this