Detecting and diagnosing energy issues for mobile applications

Li，Xueliang1; Yang，Yuming1; Liu，Yepang2; Gallagher，John P.3; Wu，Kaishun1

10.1145/3395363.3397350

2020-07-18

ISSTA 2020 - Proceedings of the 29th ACM SIGSOFT International Symposium on Software Testing and Analysis

115-127

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.

Android Energy Bugs Energy Issues Mobile Applications

其他

英语

EI

20203209015071

Energy efficiency ; Testing ; Android (operating system) ; Program debugging ; Machine learning ; Mobile computing ; Learning algorithms

Energy Conservation:525.2 ; Computer Software, Data Handling and Applications:723 ; Computer Programming:723.1 ; Artificial Intelligence:723.4 ; Machine Learning:723.4.2

2-s2.0-85088920320

Scopus

1.Shenzhen University,China
2.Southern University of Science and Technology,China
3.Roskilde University,Denmark

Li，Xueliang,Yang，Yuming,Liu，Yepang,et al. Detecting and diagnosing energy issues for mobile applications[C],2020:115-127.