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Cited by 91Mobile and Web ApplicationsGreen IT and SustainabilitySoftware System Performance and Reliability

A Survey of Performance Optimization for Mobile Applications

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Hort, Max, Kechagia, Maria, Sarro, Federica, Harman, Mark·2022·IEEE Transactions on Software Engineering
DOI 10.1109/tse.2021.3071193
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Abstract

To ensure user satisfaction and success of mobile applications, it is important to provide highly performant applications. This is particularly important for resource-constrained systems such as mobile devices. Thereby, non-functional performance characteristics, such as energy and memory consumption, play an important role for user satisfaction. This paper provides a comprehensive survey of non-functional performance optimization for Android applications. We collected 156 unique publications, published between 2008 and 2020, that focus on the optimization of performance of mobile applications. We target our search at four performance characteristics: responsiveness, launch time, memory and energy consumption. For each performance characteristic, we categorize optimization approaches based on the method used in the corresponding publications. Furthermore, we identify research gaps in the literature for future work.

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  • The reliability of benchmarking results in computer science and AI depends critically on their reproducibility, generalizability across different contexts, consistency over time, and objectivity in measurement.

    “Additionally, the reliability of benchmarking results in computer science and AI depends critically on their reproducibility, generalizability across different contexts, consistency over time, and objectivity in measurement, but at the same time, it raises the question of who holds the authority to set benchmark standards [17], [22].”

    From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [17]

  • Standardization is essential in benchmarking to ensure consistency across evaluations, making the results reliable and widely accepted within the community.

    “Standardization is essential in benchmarking to ensure consistency across evaluations, making the results reliable and widely accepted within the community [17].”

    From Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence (2026) · cites this paper as [17]

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Research works printed but not linked: 35

  1. A. Arcangeli, I. Eidus, and C. Wright, "Increasing memory density by using ksm," in Proceedings of the linux symposium. Citeseer, 2009, pp. 19-28.
  2. B. Lee, S. M. Kim, E. Park, and D. Han, "Memscope: analyzing memory duplication on android systems," in Proceedings of the 6th Asia-Pacific Workshop on Systems. ACM, 2015, p. 19.
  3. L. Ravindranath, J. Padhye, S. Agarwal, R. Mahajan, I. Obermiller, and S. Shayandeh, "Appinsight: mobile app performance moni- toring in the wild," in Presented as part of the 10th {USENIX} Sym- posium on Operating Systems Design and Implementation ({OSDI} 12), 2012, pp. 107-120.
  4. M.-R. Ra, A. Sheth, L. Mummert, P. Pillai, D. Wetherall, and R. Govindan, "Odessa: enabling interactive perception applica- tions on mobile devices," in Proceedings of the 9th international conference on Mobile systems, applications, and services. ACM, 2011, pp. 43-56.
  5. M. S. Gordon, D. A. Jamshidi, S. Mahlke, Z. M. Mao, and X. Chen, "{COMET}: Code offload by migrating execution transparently," in Presented as part of the 10th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 12), 2012, pp. 93-106.
  6. B. Choi, J. Kim, D. Cho, S. Kim, and D. Han, "Appx: an automated app acceleration framework for low latency mobile app," in Proceedings of the 14th International Conference on emerging Networking EXperiments and Technologies, 2018, pp. 27-40.
  7. R. Vallée-Rai, P. Co, E. Gagnon, L. Hendren, P. Lam, and V. Sun- daresan, "Soot: A java bytecode optimization framework," in CASCON First Decade High Impact Papers, 2010, pp. 214-224.
  8. S.-H. Kim, J. Jeong, J.-S. Kim, and S. Maeng, "Smartlmk: A memory reclamation scheme for improving user-perceived app launch time," ACM Transactions on Embedded Computing Systems (TECS), vol. 15, no. 3, pp. 1-25, 2016.
  9. Y. Joo, J. Ryu, S. Park, and K. G. Shin, "Fast: Quick application launch on solid-state drives." in FAST, 2011, pp. 259-272.
  10. Z. Zhao, M. Zhou, and X. Shen, "Satscore: Uncovering and avoiding a principled pitfall in responsiveness measurements of app launches," in Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2014, pp. 21-32.
  11. L.-Y. Tang, P.-C. Hsiu, J.-L. Huang, and M.-S. Chen, "ilauncher: an intelligent launcher for mobile apps based on individual usage patterns," in Proceedings of the 28th Annual ACM Symposium on Applied Computing, 2013, pp. 505-512.
  12. X. Zou, W. Zhang, S. Li, and G. Pan, "Prophet: What app you wish to use next," in Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication, 2013, pp. 167-170.
  13. S. Jeong, K. Lee, S. Lee, S. Son, and Y. Won, "I/o stack optimization for smartphones," in Presented as part of the 2013 USENIX Annual Technical Conference (USENIX ATC 13), 2013, pp. 309-320.
  14. A. Carroll, G. Heiser et al., "An analysis of power consumption in a smartphone." in USENIX annual technical conference, vol. 14. Boston, MA, 2010, pp. 21-21.
  15. L. Zhang, M. S. Gordon, R. P. Dick, Z. M. Mao, P. Dinda, and L. Yang, "Adel: An automatic detector of energy leaks for smart- phone applications," in Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis. ACM, 2012, pp. 363-372.
  16. G. Metri, W. Shi, M. Brockmeyer, and A. Agrawal, "Batteryex- tender: an adaptive user-guided tool for power management of mobile devices," in Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 2014, pp. 33-43.
  17. W. Jung, C. Kang, C. Yoon, D. Kim, and H. Cha, "Devscope: a nonintrusive and online power analysis tool for smartphone hardware components," in Proceedings of the eighth IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis, 2012, pp. 353-362.
  18. A. J. Pyles, Z. Ren, G. Zhou, and X. Liu, "Sifi: exploiting voip silence for wifi energy savings insmart phones," in Proceedings of the 13th international conference on Ubiquitous computing, 2011, pp. 325-334.
  19. g for wireless applications," in Proceedings of the 1st International Conference on Energy-efficient Computing and Networking, 2010, pp. 75-84.
  20. A. Saarinen, M. Siekkinen, Y. Xiao, J. K. Nurminen, M. Kemp- painen, and P. Hui, "Smartdiet: offloading popular apps to save energy," ACM SIGCOMM Computer Communication Review, vol. 42, no. 4, pp. 297-298, 2012.
  21. --, "Using Automatic Refactoring to Improve Energy Efficiency of Android Apps," in CIbSE XXI Ibero-American Conference on Software Engineering, 2018.
  22. X. Chen, K. W. Nixon, H. Zhou, Y. Liu, and Y. Chen, "Finger- shadow: An {OLED} power optimization based on smartphone touch interactions," in 6th Workshop on Power-Aware Computing and Systems (HotPower 14), 2014.
  23. K. W. Nixon, X. Chen, H. Zhou, Y. Liu, and Y. Chen, "Mobile {GPU} power consumption reduction via dynamic resolution and frame rate scaling," in 6th Workshop on Power-Aware Computing and Systems (HotPower 14), 2014.
  24. Y.-C. Chang, W.-M. Chen, P.-C. Hsiu, Y.-Y. Lin, and T.-W. Kuo, "Lsim: Ultra lightweight similarity measurement for mobile graphics applications," in Proceedings of the 56th Annual Design Automation Conference 2019, 2019, pp. 1-6.
  25. A. J. Pyles, X. Qi, G. Zhou, M. Keally, and X. Liu, "Sapsm: Smart adaptive 802.11 psm for smartphones," in Proceedings of the 2012 ACM conference on ubiquitous computing, 2012, pp. 11-20.
  26. M. Martins, J. Cappos, and R. Fonseca, "Selectively taming background android apps to improve battery lifetime," in 2015 {USENIX} Annual Technical Conference ({USENIX} {ATC} 15), 2015, pp. 563-575.
  27. A. Sinha and A. P. Chandrakasan, "Jouletrack: a web based tool for software energy profiling," in Proceedings of the 38th annual Design Automation Conference, 2001, pp. 220-225.
  28. X. Zhao, K. Rodrigues, Y. Luo, D. Yuan, and M. Stumm, "Non- intrusive performance profiling for entire software stacks based on the flow reconstruction principle," in 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), 2016, pp. 603-618.
  29. H. Kaminaga, "Improving linux startup time using software resume (and other techniques)," in Linux Symposium, 2006, p. 17.
  30. F. Chen, S. Jiang, and X. Zhang, "Smartsaver: turning flash drive into a disk energy saver for mobile computers," in Proceedings of the 2006 international symposium on Low power electronics and design, 2006, pp. 412-417.
  31. F. Chen, T. Luo, and X. Zhang, "Caftl: A content-aware flash translation layer enhancing the lifespan of flash memory based solid state drives." in FAST, vol. 11, 2011, pp. 77-90.
  32. M. Mahmoudi and S. Nadi, "The android update problem: An empirical study," in Proceedings of the 15th International Conference on Mining Software Repositories. ACM, 2018, pp. 220-230.
  33. X. Lu, X. Liu, H. Li, T. Xie, Q. Mei, D. Hao, G. Huang, and F. Feng, "Prada: prioritizing android devices for apps by mining large-scale usage data," in 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE). IEEE, 2016, pp. 3-13.
  34. C. Shepard, A. Rahmati, C. Tossell, L. Zhong, and P. Kortum, "Livelab: measuring wireless networks and smartphone users in the field," ACM SIGMETRICS Performance Evaluation Review, vol. 38, no. 3, pp. 15-20, 2011.
  35. M. Allamanis, E. T. Barr, P. Devanbu, and C. Sutton, "A survey of machine learning for big code and naturalness," ACM Computing Surveys (CSUR), vol. 51, no. 4, p. 81, 2018.

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Authors
Hort, M., Kechagia, M., Sarro, F., & Harman, M.
Affiliations
  • University College London (GB)
  • Meta (United Kingdom) (GB)
ORCID
Venue
IEEE Transactions on Software Engineering
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2022-08-01
Volume
48
Issue
8
Pages
2879-2904
ISSN
  • 0098-5589 (ISSN-L)
  • 1939-3520
  • 2326-3881
DOI
10.1109/tse.2021.3071193
OpenAlex
W3148880536
Cited by
91
References
293
Citations by year
2026: 11 · 2025: 29 · 2024: 19 · 2023: 13 · 2022: 13 · 2021: 6
FWCI
11.53
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Software Engineering (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion)
Funders
ERC Advanced fellowship (741278)
Topics
  • Physical Sciences › Computer Science › Information Systems › Mobile and Web Applications
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
  • Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
Keywords
Android application, responsiveness, memory usage, energy consumption, resource-constrained mobile devices, research gaps

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