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.






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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
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
Banker RD, Datar SM, Kemerer CF, Zweig D (1993) Software complexity and maintenance costs. Commun ACM 36(11):81–94
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
Chidamber S R, Kemerer C F (1994) A metrics suite for object oriented design. IEEE Trans Softw Eng 20(6):476–493
Cliff N (1993) Dominance statistics: Ordinal analyses to answer ordinal questions. Psychol Bull 114(3):494
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
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
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
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
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
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
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
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
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
Nielson F, Nielson HR, Hankin C (1999) Principles of Program Analysis. Springer-Verlag New York, Inc., Secaucus, NJ, USA
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
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
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
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
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Communicated by: Lin Tan
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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
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DOI: https://doi.org/10.1007/s10664-017-9507-3