Application of Fuzzy DEMATEL approach in analyzing mobile application issues
https://doi.org/10.15514/ISPRAS-2019-31(4)-5
Abstract
In the current scenario, the popularity of smartphones has led to the emergence of an ample collection of mobile applications (apps). Mobile apps are dynamic in nature; therefore, classical software development approaches are not suitable. Individual needs of the customer, new technology, battery consumption, and many more issues force app developers regularly introduce new apps to the market. But due to the unavailability of any formal and customized practices of app development, various issues occur in mobile apps. These issues may adversely affect the application and user acceptance of the end product. In this paper, fifteen issues in mobile apps have been identified. Then we applied Fuzzy-DEMATEL (Decision Making Trial and Evaluation Laboratory) method to analyze the critical mobile issues (CMIs) and divide these issues into cause and effect groups. Firstly, multiple experts evaluate the direct relations of influential issues in mobile apps. The evaluation results are presented in triangular fuzzy numbers (TFN). Secondly, convert the linguistic terms into TFN. Thirdly, based on DEMATEL, the cause-effect classifications of issues are obtained. Finally, the issues in the cause category are identified as CMIs in mobile apps. The outcome of the research is compared with the other variants of DEMATEL like G-DEMATEL and E-DEMATEL and the comparative results suggest that fuzzy-DEMATEL is the most fitting method to analyze the interrelationship of different issues in mobile apps development. The outcome of this work definitely assists the mobile apps development industry to successful identification of the serious issues where professionals and project managers could really focus on.
About the Authors
Mamta Pandey
Jaypee University of Engineering and Technology
India
Research Scholar, Department of Computer Science and Engineering
India
Research Scholar, Department of Computer Science and Engineering
Ratnesh Litorya
Jaypee University of Engineering and Technology
India
Assistant Professor, Ph.D, Department of Computer Science and Engineering
India
Assistant Professor, Ph.D, Department of Computer Science and Engineering
Prateek Pandey
Инженерно-технологический университет Джайпи
India
India
References
1. Inukollu V.N., Keshamoni D.D., Kang T., and Inukollu M. Factors Influencing Quality of Mobile Apps: Role of Mobile App Development Life Cycle. International Journal of Software Engineering and Applications, vol. 5, no. 5, 2014, pp. 15-34.
2. Pandey M., Litoriya R., Pandey P. Perception-Based Classification of Mobile Apps: A Critical Review. In Smart Computational Strategies: Theoretical and Practical Aspects. Springer, Singapore, 2019, pp. 121-133.
3. Jason Summerfield. Mobile Website vs. Mobile App: Which is Best for Your Organization? Available at: https://www.hswsolutions.com/services/mobile-web-development/mobile-website-vs-apps/.
4. Ashishdeep A., Bhatia J., Varma K. A software engineering model for mobile app development. International Journal of Computer Science and Communication, vol. 7, no. 1, 2016, pp. 150-153.
5. Pandey M., Litoriya R., Pandey P. Mobile applications in context of big data: A survey. In Proc. of the Symposium on Colossal Data Analysis and Networking (CDAN), 2016.
6. McIlroy S., Ali N., Hassan A.E. Fresh apps: an empirical study of frequently-updated mobile apps in the Google play store. Empirical Software Engineering, vol. 21, issue 3, 2016, pp.1346-1370.
7. Pandey, M., Litoriya, R., Pandey, P. Mobile APP development based on agility function. Ingénierie des Systèmes d'Information, vol. 23, no. 6, 2018, pp. 19-44.
8. Lin C.J., Wu W.W. A causal analytical method for group decision-making: Under fuzzy environment. Expert Systems with Applications, vol. 34, issue 1, 2007, pp. 205-213.
9. Litoriya, R., Kothari, A. (2013). Cost Estimation of web projects in context with Agile paradigm: Improvements and validation. International Journal of Software Engineering (A Publication of Software Engineering Competence Center - Egypt), 6(2), 91-114.
10. Minelli R. and Lanza M. Software analytics for mobile applications - Insights & Lessons Learned. In Proc. of the 17th European Conference on Software Maintenance and Reengineering, 2013, pp 144-153.
11. Stepanova E., Kirikova M. Continuous requirements engineering for mobile application development. In Joint Proceedings of Workshops, Doctoral Symposium, Research Method Track, and Poster Track co-located with the 23rd International Conference on Requirements Engineering: Foundation for Software Quality (REFSQ 2017), 2017.
12. Seyff N., Graf F. User-driven requirements engineering for mobile social software. In Software Engineering (Workshops), 2010, pp. 503-512.
13. Kryukov A.P., Demichev A.P. Decentralized Data Storages: Technologies of Construction. Programming and Computer Software, vol. 44, no. 5, 2018, pp. 303-315.
14. Kaufman J. Principles of Mobile App Design. Apteligent White Paper, 2016, 20 p. Available at: https://www.apteligent.com/wp-content/uploads/2016/07/PRINCIPLES-MOBILE-APP-DESIGN-WP.pdf.
15. Armenta-Cano F.A., Tchernykh A., Cortés-Mendoza, J.M., Yahyapour R., Drozdov A.Y., Bouvry P., Nesmachnow S. Min_c: Heterogeneous concentration policy for energy-aware scheduling of jobs with resource contention. Programming and Computer Software, vol. 43, no. 3, 2017, pp. 204-215.
16. Tchernykh A., Schwiegelsohn U., Talbi E.G., Babenk, M. Towards understanding uncertainty in cloud computing with risks of confidentiality, integrity, and availability. Journal of Computational Science. Available online 22 November 2016, DOI: 10.1016/j.jocs.2016.11.011.
17. Mavi R.K., Standing C. Critical success factors of sustainable project management in construction: A fuzzy DEMATEL-ANP approach. Journal of Cleaner Production, vol. 194, no. 1, 2018, pp. 751-765.
18. Goel S., Nagpal R., Mehrotra D. Mobile applications usability parameters: Taking an insight view. In Proc. of the International conference on Information and Communication Technology for Sustainable Development, 2018, pp. 35-43.
19. Han W.M., Hsu C.H., Yeh C.Y. Using DEMATEL to analyze the quality characteristics of mobile applications. In Proc. of the International Conference on Future Information Engineering and Manufacturing Science, 2014, pp. 131-134.
20. Sugiyanto S., Rochimah S. Integration of DEMATEL and ANP methods for calculate the weight of characteristics software quality based model ISO 9126. In Proc. of the International Conference on Information Technology and Electrical Engineering, 2013, pp. 143-148.
21. Bijoyeta Roy, S.K. Misra, Preeti Gupta, Akanksha Goswami. An Integrated DEMATEL and AHP approach for personnel estimation. International Journal of Computer Science and Information Technology & Security, vol. 2, no. 6, 2012, pp. 1206-1212.
22. Wu W.W., Lan L.W., and Lee Y.T. Exploring decisive factors affecting an organization’s SaaS adoption: A case study. International Journal of Information Management, vol. 31, issue 6, 2011, pp. 556-563.
23. Venkatesh V.G., Zhang A., Luthra S., Dubey R., Subramanian N., Mangla S. Barriers to coastal shipping development: An Indian perspective. Transportation Research. Part D: Transport and Environment, vol. 52, part A, 2017, pp.362-378.
24. Pandey P., Litoriya R., Tiwari A. A framework for fuzzy modelling in agricultural diagnostics. Journal Européen des Systèmes Automatisés, vol. 51, no. 4-6, 2018, pp. 203-223.
25. Wu Y.C., Lin C.W. National port competitiveness: Implications for India. Management Decision, vol. 46, no. 10, 2008, pp. 1482-1507.
26. Han Y., Deng Y. An enhanced fuzzy evidential DEMATEL method with its application to identify critical success factors. Soft Computing, vol. 22, no. 15, 2018, pp. 5073–5090.
27. Bhatia M.S., Srivastava R.K. Analysis of external barriers to remanufacturing using grey-DEMATEL approach: An Indian perspective. Resources, Conservation & Recycling, vol. 136, 2018, pp. 79-87.
28. Zhang L., Huang X.Y., Jiang J., Hu Y.K. CSLabel: An Approach for Labelling Mobile App Reviews. Journal of Computer Science and Technology, vol. 32, no. 6, 2017, pp. 1076–1089.
29. Maalej W., Kurtanovic Z., Nabil H., Stanik C. On the automatic classification of app reviews. Requirement Engineering, vol. 21, no. 3, 2015, pp. 311-331.
30. Gui J., McIlroy S., Nagappan M., and Halfond W.G. Truth in advertising: The hidden cost of mobile ads for software developers. In Proc. of the 37th International Conference on Software Engineering, 2015, pp. 100-110.
31. Xu X., Dutta K., and Datta A. Functionality-based mobile app recommendation by identifying aspects from user reviews. In Proc. of the 35th International Conference on Information Systems, 2014, pp. 1-10.
32. Khalid H. et al. What Do Mobile App Users Complain About? A Study on Free iOS Apps. IEEE software, vol. 32, no. 3): (2014) 1-6
33. Tan S.H. et al. Repairing Crashes in Android Apps. In Proc. of the 40th International Conference on Software Engineering, 2018, pp. 187-198.
34. Rajput G.S., Litoriya R. Corad Agile Method for Agile Software Cost Estimation. Open Access Library Journal, vol. 1, no.3, 2014, pp. 1-13.
35. Vu P.M. et al. Mining user opinions in mobile app reviews: A keyword-based approach. In Proc. of the International Conference on Automated Software Engineering, 2015, pp. 749-759
36. Zhang T. et al. Compatibility testing service for mobile applications. In Proc. of the IEEE Symposium on Service-Oriented System Engineering, 2015, pp. 179-186.
37. Bonne B. et al. Insecure Network, Unknown Connection: Understanding Wi-Fi Privacy Assumptions of Mobile Device Users. Information, vol. 8, no. 3, 2017, pp. 1-20.
38. Vu P.M. Mining user opinions in mobile app reviews: A keyword-based approach. In Proc. of the International Conference on Automated Software Engineering, 2015, pp. 749-759.
39. Wilke C. et al. Energy consumption and efficiency in mobile applications: A user feedback study. In Proc. of the IEEE International Conference on Green Computing and Communications and IEEE International Conference on Internet of Things, and IEEE International Conference on Cyber, Physical and Social Computing, 2013, pp. 134-141.
40. Datta S.K., Bonnet C., and Nikaein N. Android Power Management: Current and Future Trends. In Proc. of the First IEEE Workshop on Enabling Technologies for Smartphone and Internet of Things, 2012, pp. 48-53.
41. Pandey M., Litoriya R., Pandey P. Novel Approach for Mobile Based App Development Incorporating MAAF. Wireless Personal Communications, vol. 107, issue 4, 2019, pp. 1687–1708.
42. Ferreira D., Dey A.K. and Kostakos V. Understanding Human-Smartphone Concerns: A Study of Battery Life. Lecture Notes in Computer Science, vol. 6696, 2011, pp. 19–33.
43. Willocx M., Vossaert J. and Naessens V. Comparing performance parameters of mobile app development strategies. In Proc. of the International Conference on Mobile Software Engineering and Systems, 2016, pp 38-47.
44. Litoriya R., Sharma N., Kothari A. Incorporating Cost driver substitution to improve the effort using Agile COCOMO II. In Proc. of the CSI Sixth International Conference on Software Engineering, 2012, pp. 1-7.
45. Falaki H., Lymberopoulos D, and Mahajan R. A First Look at Traffic on Smartphones. In Proc. of the 10th ACM SIGCOMM conference on Internet measurement, 2010, pp. 281-287.
46. Comino S., Manenti F.M., and Mariuzzo F. (2015) Updates Management in Mobile Applications. iTunes vs Google Play. Available at SSRN: https://ssrn.com/abstract=2664463 or http://dx.doi.org/10.2139/ssrn.2664463.
47. Hassan S., Shang W., and Hassan A.E. An empirical study of emergency updates for top android mobile apps. Empirical Software Engineering, vol. 22, no. 1, 2017, pp. 505-546.
48. Ranjan A., Litoriya R. Relational Algebra Interpreter in Context of Query Languages. International Journal of Computer Theory and Engineering, vol. 3, no. 1, 2011, pp. 9-15.
49. Andreou A.S. et al. Key issues for the design and development of mobile commerce services and applications. International Journal of Mobile Communications, vol. 3, no. 3, 2005, pp. 303–323.
50. Perez B.M., Diez I.D., and Coronado M.L. Privacy and security in mobile health apps: A review and recommendations. Journal of Medical Systems, vol. 39, no. 1, 2017, pp. 1-8.
51. Pandey P., Litoriya R. An activity vigilance system for elderly based on fuzzy probability transformations. Journal of Intelligent and Fuzzy Systems, vol. 36, no. 3, 2019, pp. 2481-2494.
52. Armand A., Allahviranloo T., Gouyandeh Z. Some Fundamental Results on Fuzzy Calculus. Iranian Journal of Fuzzy Systems, vol. 15, no. 3, 2018, pp. 27-46.
53. Pandey P., Kumar S., Shrivastav S. A fuzzy decision making approach for analogy detection in new product forecasting. Journal of Intelligent & Fuzzy Systems, vol. 28, no. 5, 2015, pp. 2047-2057.
54. Bhadauriya S., Sharma V., Litoriya R. Empirical Analysis of Ethical Issues in the Era of Future Information Technology. In Proc. of the 2nd International Conference on Software Technology and Engineering, vol. 2, 2010, pp. 31-35.
55. Tseng M.L., Wu K.J., Nguyen T.H. Information technology in supply chain management: a case study. Procedia - Social and Behavioral Sciences, vol. 25, 2011, pp. 257-272.
56. Deng J.L. Control problems of grey systems. Systems and Control Letters, vol. 1, no. 5, 1982, pp. 288–294.
57. Tseng M.L. A causal and effect decision making model of service quality expectation using grey-fuzzy DEMATEL approach. Expert Systems with Applications, vol. 36, no. 4, 2009, pp. 7738-7748.
58. Pandey P., Kumar S., Shrivastav S. Forecasting using Fuzzy Time Series for Diffusion of Innovation: Case of Tata Nano Car in India. National Academy Science Letters, vol. 36, no. 3, 2013, pp. 299-309.
59. Pandey P., Kumar S., Shrivastav S. A fuzzy decision making approach for analogy detection in new product forecasting. Journal of Intelligent and Fuzzy Systems, vol. 28, no. 5, 2015, pp. 2047-2057.
60. Wang F. et al. A semantics-based approach to multi-source heterogeneous information fusion in the internet of things. Soft Computing, vol. 21, no. 8, 2017, pp. 2005–2013.
61. Zheng X. and Deng Y. Dependence assessment in human reliability analysis based on evidence credibility decay model and IOWA operator. Annals of Nuclear Energy, vol. 112, 2018, pp. 673-684.
62. Liu T., Deng Y., Chan F. (2018) Evidential supplier selection based on DEMATEL and game theory. International Journal of Fuzzy Systems, vol. 20, no. 4, 2018, pp. 1321-1333.
63. Han Y., Deng Y. A hybrid intelligent model for assessment of critical success factors in high-risk emergency system. Journal of Ambient Intelligence and Humanized Computing, vol. 9, issue 6, 2018, pp 1933–1953.
64. Liu Z. et al. Combination of classifiers with optimal weight based on evidential reasoning. IEEE Transactions on Fuzzy Systems, vol. 26, issue 3, 2018, pp. 1217-1230.
65. Song Y. et al. Combination of interval-valued belief structures based on intuitionist fuzzy set. IEEE Transactions on Fuzzy Systems, vol. 26, issue: 3, 2018, pp. 61–70.
66. Wang J., Wu J., Wang J., Zhang H., Chen X. Multi-criteria decision-making methods based on the hausdorff distance of hesitant fuzzy linguistic numbers. Soft Computing, vol. 20, no. 4, 2016, pp. 1621–1633
67. Pandey M, Litoriya R., and Pandey P. An ISM approach for modeling the issues and factors of mobile app development. International Journal of Software Engineering and Knowledge Engineering, vol. 28, no. 7, 2018, pp. 937-953.
Review
For citations:
Pandey M., Litorya R., Pandey P. Application of Fuzzy DEMATEL approach in analyzing mobile application issues. Proceedings of the Institute for System Programming of the RAS (Proceedings of ISP RAS). 2019;31(4):83-96. (In Russ.) https://doi.org/10.15514/ISPRAS-2019-31(4)-5
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