research-article

NavyDroid: Detecting Energy Inefficiency Problems for Smartphone Applications

Authors:

Xiaoxing MaAuthors Info & Claims

Internetware '17: Proceedings of the 9th Asia-Pacific Symposium on Internetware

Article No.: 8, Pages 1 - 10

https://doi.org/10.1145/3131704.3131705

Published: 23 September 2017 Publication History

Abstract

Many smartphone applications suffer from energy inefficiency problems, but locating these problems is quite difficult and labor-intensive. Automated tools for detecting energy inefficiency bugs have been shown to be effective. Existing approaches generally consist of two parts, namely the simulation part and the monitor part. The simulation part explores an application's state space guided by an application execution model, and the monitor part checks for occurrences of energy inefficiency patterns. However, existing approaches might miss energy inefficiency bugs due to their imprecise application execution models and oversimplified energy inefficiency diagnosis policies. In this paper, we proposed NavyDroid, an approach to diagnosing energy inefficiency problems more effectively. We summarized a comprehensive application execution model from Android specifications and expressed it as a state machine. By considering multiple patterns of wake lock misuses, our approach is able to detect more complex energy bugs caused by wake lock misuses. We implemented NavyDroidon top of Java Pathfinder (JPF) and applied it to real-world applications. We evaluated NavyDroid with 17 real-world Android applications, and NavyDroid located more energy inefficiency bugs in these applications than the existing work E-GreenDroid did. The results of our experiments demonstrate that our approach can effectively locate real energy inefficiency bugs in Android applications, suggesting its effectiveness.

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Digital Library

Cited By

Li XChen JLiu YWu KGallagher J(2023)Combatting Energy Issues for Mobile ApplicationsACM Transactions on Software Engineering and Methodology10.1145/352785132:1(1-44)Online publication date: 13-Feb-2023
https://dl.acm.org/doi/10.1145/3527851
Wu ZChen XLee S(2023)A systematic literature review on Android-specific smellsJournal of Systems and Software10.1016/j.jss.2023.111677201:COnline publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.jss.2023.111677
Sun YFang JChen YLiu YChen ZGuo SChen XTan Z(2022)Energy inefficiency diagnosis for Android applications: a literature reviewFrontiers of Computer Science10.1007/s11704-021-0532-417:1Online publication date: 8-Aug-2022
https://doi.org/10.1007/s11704-021-0532-4
Show More Cited By

Index Terms

NavyDroid: Detecting Energy Inefficiency Problems for Smartphone Applications
1. Software and its engineering
  1. Software creation and management
    1. Software verification and validation
      1. Software defect analysis
        Software testing and debugging
  2. Software organization and properties
    1. Extra-functional properties
      1. Software performance

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cover image ACM Other conferences

Internetware '17: Proceedings of the 9th Asia-Pacific Symposium on Internetware

September 2017

172 pages

ISBN:9781450353137

DOI:10.1145/3131704

Conference Chairs:
Hong Mei,
Jian Lyu,
Zhi Jin,
Wenyun Zhao

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Fudan University
SIGSOFT: ACM Special Interest Group on Software Engineering
CCF: China Computer Federation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 September 2017

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Internetware'17

Internetware'17: The Eighth Asia-Pacific Symposium on Internetware

September 23, 2017

Shanghai, China

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Overall Acceptance Rate 55 of 111 submissions, 50%

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Cited By

Li XChen JLiu YWu KGallagher J(2023)Combatting Energy Issues for Mobile ApplicationsACM Transactions on Software Engineering and Methodology10.1145/352785132:1(1-44)Online publication date: 13-Feb-2023
https://dl.acm.org/doi/10.1145/3527851
Wu ZChen XLee S(2023)A systematic literature review on Android-specific smellsJournal of Systems and Software10.1016/j.jss.2023.111677201:COnline publication date: 1-Jul-2023
https://dl.acm.org/doi/10.1016/j.jss.2023.111677
Sun YFang JChen YLiu YChen ZGuo SChen XTan Z(2022)Energy inefficiency diagnosis for Android applications: a literature reviewFrontiers of Computer Science10.1007/s11704-021-0532-417:1Online publication date: 8-Aug-2022
https://doi.org/10.1007/s11704-021-0532-4
Khan MLee SWu ZAbbas S(2021)Wake Lock Leak Detection in Android Apps Using Multi-Layer PerceptronElectronics10.3390/electronics1018221110:18(2211)Online publication date: 9-Sep-2021
https://doi.org/10.3390/electronics10182211
Naseer ANadeem AUz Zaman Q(2021)A GUI Based Approach to Detect Energy Bugs in Android Applications2021 16th International Conference on Emerging Technologies (ICET)10.1109/ICET54505.2021.9689870(1-6)Online publication date: 22-Dec-2021
https://doi.org/10.1109/ICET54505.2021.9689870
Shahab ANaseer AZafar MNadeem A(2021)Detection of Energy Bugs in Android Applications: A Systematic Literature Review2021 International Conference on Frontiers of Information Technology (FIT)10.1109/FIT53504.2021.00012(7-12)Online publication date: Dec-2021
https://doi.org/10.1109/FIT53504.2021.00012
Khan MLee SAbbas SAbbas ABashir A(2021)Detecting Wake Lock Leaks in Android Apps Using Machine LearningIEEE Access10.1109/ACCESS.2021.31102449(125753-125767)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3110244
Le H(2021)Analyzing Energy Leaks of Android Applications Using Event-BMobile Networks and Applications10.1007/s11036-021-01764-y26:3(1329-1338)Online publication date: 23-May-2021
https://dl.acm.org/doi/10.1007/s11036-021-01764-y
Sanchez ADelgado-Perez PMedina-Bulo ISegura S(2020)TANDEM: A Taxonomy and a Dataset of Real-World Performance BugsIEEE Access10.1109/ACCESS.2020.30009288(107214-107228)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3000928

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