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Energy inefficiency diagnosis for Android applications: a literature review

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Abstract

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.

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Acknowledgements

This work was supported by the Guangdong Basic and Applied Basic Research Foundation (2021A1515012297), the Shenzhen Science and Technology Innovation Commission (R2020A045), and the Open Project of Guangdong Provincial Key Laboratory of High-Performance Computing(2021).

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Authors and Affiliations

  1. Department of Computer Science, Jinan University, Guangzhou, 510632, China

    Yuxia Sun, Jiefeng Fang, Yanjia Chen, Zhao Chen, Xinkai Chen & Ziyuan Tan

  2. Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China

    Yepang Liu

  3. The Department of Computing, Hong Kong Polytechnic University, Hong Kong, 999077, China

    Song Guo

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  1. Yuxia Sun
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Correspondence to Yepang Liu.

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Yuxia Sun received the BS degree from the Department of Computer Science, Huazhong University of Science and Technology, China and the PhD degree from the Department of Computer Science, Sun Yat-sen University, China. She is currently an Associate Professor with the Department of Computer Science, Jinan University, China. She was a Research Associate with Hong Kong Polytechnic University, China, and the University of Hong Kong, China and a Research Scholar with the College of Computing, Georgia Institute of Technology, USA. Her current research interests include software engineering, software safety, and system safety.

Jiefeng Fang received the BS degree in computer science from South China Agricultural University, China. She is currently a master student in the Department of Computer Science, Jinan University, China. Her research interests include software testing and software safety.

Yanjia Chen received the MS degree in computer science from Jinan University, China. Her research interests include software testing and software safety.

Yepang Liu received the doctoral degree in computer science and engineering from The Hong Kong University of Science and Technology (HKUST), China. In 2018, he joined the Department of Computer Science and Engineering of the Southern University of Science and Technology (SUSTech), China as a tenure-track assistant professor. His research interests include software testing and analysis, empirical software engineering, cyber-physical systems, mobile computing, and cybersecurity. He published widely in top software engineering venues and has received three ACM SIGSOFT Distinguished Paper Awards. He also participates actively in program and organizing committees of major international conferences and received ACM SIGSOFT Service Award for his contribution to the successful organization of the 22nd ACM SIGSOFT International Symposium on the Foundations of Software Engineering (FSE 2014).

Zhao Chen is currently a master student in the Department of Computer Science, Jinan University, China. His current research interests include software testing.

Song Guo received the PhD degree in computer science from the University of Ottawa, Canada in 2005. He was a Professor with The University of Aizu, Japan. He is currently a Full Professor and the Associate Head with the Department of Computing, The Hong Kong Polytechnic University, China. His research interests are mainly in the areas of big data, edge AI, cloud computing, mobile computing, and distributed systems. Prof. Guo was an IEEE ComSoc Distinguished Lecturer from 2017 to 2018 and served on the IEEE ComSoc Board of Governors from 2018 to 2019. He has also served as the general and the program chair for numerous IEEE conferences. He is a fellow of the Canadian Academy of Engineering. He was a recipient of the 2019 IEEE TCBD Best Conference Paper Award, the 2018 IEEE TCGCC Best Magazine Paper Award, the 2017 IEEE SYSTEMS JOURNAL Annual Best Paper Award, and other six best paper awards from the IEEE/ACM conferences. His work was also recognized by the 2016 Annual Best of Computing: Notable Books and Articles in Computing in ACM Computing Reviews. He is the Editor-in-Chief of the IEEE Open Journal of the Computer Society and an Associate Editor of the IEEE Transactions on Cloud Computing, the IEEE Transactions on Sustainable Computing, and the IEEE Transactions on Green Communications and Networking.

Xinkai Chen received the BS degree in computer science from Jinan University, China. His research interest is software testing.

Ziyuan Tan received the BS degree in computer science from Jinan University, China. His current research interests include software safety and computer vision.

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Sun, Y., Fang, J., Chen, Y. et al. Energy inefficiency diagnosis for Android applications: a literature review. Front. Comput. Sci. 17, 171201 (2023). https://doi.org/10.1007/s11704-021-0532-4

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