Skip to main content
SpringerLink
Log in

A study of the relation of mobile device attributes with the user-perceived quality of Android apps

  • Published:
Empirical Software Engineering Aims and scope Submit manuscript

Abstract

The number of mobile applications (apps) and mobile devices has increased considerably over the past few years. Online app markets, such as the Google Play Store, use a star-rating mechanism to quantify the user-perceived quality of mobile apps. Users may rate apps on a five point (star) scale where a five star-rating is the highest rating. Having considered the importance of a high star-rating to the success of an app, recent studies continue to explore the relationship between the app attributes, such as User Interface (UI) complexity, and the user-perceived quality. However, the user-perceived quality reflects the users’ experience using an app on a particular mobile device. Hence, the user-perceived quality of an app is not solely determined by app attributes. In this paper, we study the relation of both device attributes and app attributes with the user-perceived quality of Android apps from the Google Play Store. We study 20 device attributes, such as the CPU and the display size, and 13 app attributes, such as code size and UI complexity. Our study is based on data from 30 types of Android mobile devices and 280 Android apps. We use linear mixed effect models to identify the device attributes and app attributes with the strongest relationship with the user-perceived quality. We find that the code size has the strongest relationship with the user-perceived quality. However, some device attributes, such as the CPU, have stronger relationships with the user-perceived quality than some app attributes, such as the number of UI inputs and outputs of an app. Our work helps both device manufacturers and app developers. Manufacturers can focus on the attributes that have significant relationships with the user-perceived quality. Moreover, app developers should be careful about the devices for which they make their apps available because the device attributes have a strong relationship with the ratings that users give to apps.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. http://www.gsmarena.com/

  2. http://www.phonearena.com/

References

  • Albrecht AJ, Gaffney Jr JE (1983) Software function, source lines of code, and development effort prediction: a software science validation. IEEE Trans on Softw Eng 9(6):639–648

    Article  Google Scholar 

  • Apple (2015) itunes. https://www.apple.com/itunes/

  • Balasubramanian N, Balasubramanian A, Venkataramani A (2009) Energy consumption in mobile phones: a measurement study and implications for network applications. In: Proceedings of the 9th ACM SIGCOMM conference on Internet measurement, ACM, pp 280–293

  • Bandi RK, Vaishnavi K, Turk DE (2003) Predicting maintenance performance using object-oriented design complexity metrics. IEEE Trans Softw Eng 29(1):77–87

    Article  Google Scholar 

  • Banker RD, Datar SM, Kemerer CF, Zweig D (1993) Software complexity and maintenance costs. Commun ACM 36(11):81–94

    Article  Google Scholar 

  • Bavota G, Linares-Vasquez M, Bernal-Cardenas C E, Penta M D, Oliveto R, Poshyvanyk D (2015) The impact of api change-and fault-proneness on the user ratings of android apps. IEEE Trans Softw Eng 41(4):384–407

    Article  Google Scholar 

  • Chidamber S R, Kemerer C F (1994) A metrics suite for object oriented design. IEEE Trans Softw Eng 20(6):476–493

    Article  Google Scholar 

  • Cliff N (1993) Dominance statistics: Ordinal analyses to answer ordinal questions. Psychol Bull 114(3):494

    Article  Google Scholar 

  • Coppick J C, Cheatham T J (1992) Software metrics for object-oriented systems. In: Proceedings of the 1992 ACM annual conference on communications, ACM, pp 317–322

  • Cormen T H, Leiserson C E, Rivest RL, Stein C (2009) Introduction to algorithms. MIT press

  • Costa-Montenegro E, Barragáns-Martínez A B, Rey-López M (2012) Which app? a recommender system of applications in markets: implementation of the service for monitoring users’ interaction. Expert Syst Appl 39(10):9367–9375

    Article  Google Scholar 

  • Dex2jar J (2016) Java decompiler. http://jd.benow.com/

  • Draper NR, Smith H, Pownell E (1966) Applied regression analysis, vol 3. Wiley, New York

  • Dunn O J (1964) Multiple comparisons using rank sums. Technometrics 6 (3):241–252

    Article  Google Scholar 

  • Faraway JJ (2005) Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models. CRC press

  • Galvis Carreño LV, Winbladh K (2013) Analysis of user comments: an approach for software requirements evolution. In: Proceedings of the 35th international conference on software engineering, IEEE, ICSE ’13, pp 582–591

  • Google (2015a) Application fundamentals. http://developer.android.com/guide/components/fundamentals.html/

  • Google (2015b) Google play store. http://play.google.com/

  • Google (2015c) Layouts. http://developer.android.com/intl/ru/guide/topics/ui/declaring-layout.html/

  • Google (2015d) See your app’s ratings & reviews. https://support.google.com/googleplay/android-developer/answer/138230

  • Google (2015e) Supporting multiple screens. http://developer.android.com/guide/practices/

  • Han D, Zhang C, Fan X, Hindle A, Wong K, Stroulia E (2012) Understanding android fragmentation with topic analysis of vendor-specific bugs. In: Proceedings of the 19th working conference on reverse engineering, IEEE, pp 83–92

  • Harbach M, Hettig M, Weber S, Smith M (2014) Using personal examples to improve risk communication for security & privacy decisions. In: Proceedings of the 32nd annual ACM conference on human factors in computing systems, ACM, pp 2647–2656

  • Harman M, Jia Y, Zhang Y (2012) App store mining and analysis: Msr for app stores. In: Proceedings of the 9th IEEE working conference on mining software repositories, IEEE, MSR ’12, pp 108–111

  • Harrell (2015) Harrell. http://cran.r-project.org/web/packages/Hmisc/index.html

  • Harrell F E (2001) Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. Springer

  • Hassenzahl M, Tractinsky N (2006) User experience - a research agenda. Behav Inform Technol 25(2):91–97

    Article  Google Scholar 

  • Iacob C, Harrison R (2013) Retrieving and analyzing mobile apps feature requests from online reviews. In: Proceedings of the 10th working conference on mining software repositories, IEEE, MSR ’13, pp 41–44

  • Ickin S, Wac K, Fiedler M, Janowski L, Hong J H, Dey A K (2012) Factors influencing quality of experience of commonly used mobile applications. IEEE Commun Mag 50(4):48–56

    Article  Google Scholar 

  • Dex2ja J (2016) Java decompiler. http://jd.benow.com/

  • Johnson R E, Foote B (1988) Designing reusable classes. Object-oriented Program 1(2):22–35

    Google Scholar 

  • Kaup F, Hausheer D (2013) Optimizing energy consumption and qoe on mobile devices. In: Proceedings of the 21st IEEE international conference on network protocols, IEEE, pp 1–3

  • Khalid H, Nagappan M, Shihab E, Hassan AE (2014) Prioritizing the devices to test your app on: a case study of android game apps. In: Proceedings of the 22nd ACM SIGSOFT international symposium on the foundations of software engineering, pp 370–379

  • Kim HW, Lee H, Son J (2011) An exploratory study on the determinants of smartphone app purchase. In: Proceedings of the 11th international decision science institute and the 16th Asia pacific decision sciences institute joint meeting

  • Korhonen H, Arrasvuori J, Väänänen-Vainio-Mattila K (2010) Analysing user experience of personal mobile products through contextual factors. In: Proceedings of the 9th international conference on mobile and ubiquitous multimedia, ACM, New York, NY, USA, MUM ’10, pp 11:1–11:10

  • Kruskal W H, Wallis W A (1952) Use of ranks in one-criterion variance analysis. J Am Stat Assoc 47(260):583–621

    Article  MATH  Google Scholar 

  • Lawson S (2009) Android market needs more filters, t-mobile says. http://www.pcworld.com/article/161410/article.html

  • Li D, Halfond WG (2014) An investigation into energy-saving programming practices for android smartphone app development. In: Proceedings of the 3rd international workshop on green and sustainable software, ACM, pp 46–53

  • Li D, Hao S, Gui J, Halfond WG (2014) An empirical study of the energy consumption of android applications. In: Proceedings of the 30th IEEE international conference on software maintenance and evolution, IEEE, pp 121–130

  • Li W, Henry S (1993) Object-oriented metrics that predict maintainability. J Syst Softw 23(2):111–122

    Article  Google Scholar 

  • Linares-Vásquez M, Bavota G, Bernal-Cárdenas C, Di Penta M, Oliveto R, Poshyvanyk D (2013) Api change and fault proneness: a threat to the success of android apps. In: Proceedings of the 9th joint meeting on european software engineering and foundations of software engineering, ACM, ESEC/FSE 2013, pp 477–487

  • Martin W, Harman M, Jia Y, Sarro F, Zhang Y (2015) The app sampling problem for app store mining. In: Proceedings of the 12th working conference on mining software repositories, IEEE, pp 123–133

  • Martin W, Sarro F, Jia Y, Zhang Y, Harman M (to appear) A survey of app store analysis for software engineering. IEEE Trans Softw Eng

  • McCabe T J (1976) A complexity measure. IEEE Trans Softw Eng SE 2(4):308–320

    Article  MathSciNet  MATH  Google Scholar 

  • McDonnell T, Ray B, Kim M (2013) An empirical study of api stability and adoption in the android ecosystem. In: Proceedings of the 29th IEEE international conference on software maintenance, IEEE, pp 70–79

  • Miecznikowski J, Hendren L (2002) Decompiling java bytecode: problems, traps and pitfalls. In: Horspool R (ed) Proceedings of the compiler construction, lecture notes in computer science, vol 2304. Springer, Berlin Heidelberg, pp 111–127

  • Moran K, Linares-Vásquez M, Bernal-Cárdenas C, Poshyvanyk D (2015) Auto-completing bug reports for android applications. In: Proceedings of the 10th joint meeting on european software engineering and foundations of software engineering, ACM, New York, NY, USA, ESEC/FSE 2015, pp 673–686

  • Nakagawa S, Schielzeth H (2013) A general and simple method for obtaining r2 from generalized linear mixed-effects models. Methods Ecol Evol 4(2):133–142

    Article  Google Scholar 

  • Nielson F, Nielson HR, Hankin C (1999) Principles of Program Analysis. Springer-Verlag New York, Inc., Secaucus, NJ, USA

    Book  MATH  Google Scholar 

  • Noei E, Syer MD, Zou Y, Hassan AE, Keivanloo I (2016) A study of the relation of mobile device attributes with the user-perceived quality of android apps, http://sailhome.cs.queensu.ca/replication/mobile_device_attributes/

  • Pagano D, Maalej W (2013) User feedback in the appstore: an empirical study. In: Proceedings of the 21st IEEE international requirements engineering conference, IEEE, pp 125–134

  • Pinheiro J, Bates D (2006) Mixed-effects models in S and S-PLUS. Springer Science & Business Media

  • Pinheiro J, Bates D, DebRoy S, Sarkar D, et al. (2007) Linear and nonlinear mixed effects models. R package version 3:57

    Google Scholar 

  • Selenium (2014) Selenium - web browser automation. http://seleniumhq.org/

  • Shabtai A, Fledel Y, Elovici Y (2010) Automated static code analysis for classifying android applications using machine learning. In: Proceedings of the 2010 international conference on computational intelligence and security. IEEE, pp 329–333

  • Shull F, Singer J, Sjøberg DI (2007) Guide to Advanced Empirical Software Engineering. Springer-Verlag New York, Inc., Secaucus, NJ, USA

    Google Scholar 

  • Stats A (2016) Number of android applications. http://www.appbrain.com/stats/number-of-android-apps

  • Syer MD, Adams B, Zou Y, Hassan AE (2011) Exploring the development of micro-apps: a case study on the blackberry and android platforms. In: Proceedings of the 11th IEEE international working conference on source code analysis and manipulation, IEEE, pp 55–64

  • Syer MD, Nagappan M, Hassan AE, Adams B (2013) Revisiting prior empirical findings for mobile apps: an empirical case study on the 15 most popular open-source android apps. In: Proceedings of the conference of the center for advanced studies on collaborative research, pp 283–297

  • Syer M D, Nagappan M, Adams B, Hassan A E (2014) Studying the relationship between source code quality and mobile platform dependence. Softw Qual 23(3):485–508

    Article  Google Scholar 

  • Taba SES, Keivanloo I, Zou Y, Ng JW, Ng T (2014) An exploratory study on the relation between user interface complexity and the perceived quality. In: Proceedings of the 14th international conference on Web engineering, pp 370–379

  • Wasserman AI (2010) Software engineering issues for mobile application development. In: Proceedings of the FSE/SDP workshop on future of software engineering research, ACM, New York, NY, USA, FoSER ’10, pp 397–400

  • Winter B (2013) A very basic tutorial for performing linear mixed effects analyses. arXiv:13085499

  • Zuur A, Ieno E N, Walker N, Saveliev AA, Smith GM (2009) Mixed effects models and extensions in ecology with R. Springer Science & Business Media

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Queen’s University, Kingston, Canada

    Ehsan Noei, Ying Zou & Iman Keivanloo

  2. School of Computing, Queen’s University, Kingston, Canada

    Mark D. Syer & Ahmed E. Hassan

Authors
  1. Ehsan Noei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mark D. Syer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ying Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ahmed E. Hassan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Iman Keivanloo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ehsan Noei.

Additional information

Communicated by: Lin Tan

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Noei, E., Syer, M.D., Zou, Y. et al. A study of the relation of mobile device attributes with the user-perceived quality of Android apps. Empir Software Eng 22, 3088–3116 (2017). https://doi.org/10.1007/s10664-017-9507-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10664-017-9507-3

Keywords

Search

Navigation