The Role of App Update and User Feedback
DOI:
https://doi.org/10.61841/pmhvwf83Keywords:
Anticipate, data-driven decisions, release notes, non-informative, empirical, irrelevant, bug reportsAbstract
Developing a mobile app that users will love can be a challenging task even with a great idea and a talented development team. It’s impossible to anticipate every user’s need or preference. That’s where user feedback comes in collecting feedback from app users is essential for improving app’s functionality, user experience, and overall success. It provides us with valuable insights into what users like, what they don’t like, and what they want to see improved. In turn, this information allows us to make data-driven decisions about our app’s development. Our work explores the rule of user reviews in app updates based on release notes. For this purpose, we collected user reviews and released notes for Spotify, the number one app in the music category in apple app store, as the research data. Then, we manually removed non-informative parts of each release notes and manually determined the relevance of the app reviews with respect to the release notes. Our empirical results show that more than 60% of the matched reviews are actually irrelevant to the corresponding release notes. When zooming in at these relevant user reviews, we found that around half of them were posted before the new release and referred to requests, suggestions, and complaints. Whereas the other half of the relevant user reviews were posted after updating the apps and concentrated more on bug reports and praise.
Downloads
References
1. Carreño, L.V.G., Winbladh, K.: Analysis of user comments: an approach for software requirements evolution. In: International Conference on Software Engineering, pp. 582–591. IEEE (2013)
2. Cerf, V.G.: Apps and the web. Commune. ACM 59(2), 7–7 (2016)
3. Chen, N., Lin, J., Hoi, S.C., Xiao, X., Zhang, B.: Ar-miner: mining informative reviews for developers from mobile app. marketplace. In: International Conference on Software Engineering, pp. 767–778. ACM (2014)
4. Cugola, G., Ghezzi, C., Pinto, L.S., Tamburrelli, and G.: Adaptive service-oriented mobile applications: A declarative approach. In: International Conference on Service-Oriented Computing, pp. 607–614. Springer (2012)
5. R. K. Kaushik Anjali and D. Sharma, "Analyzing the Effect of Partial Shading on Performance of Grid Connected Solar PV System", 2018 3rd International Conference and Workshops on Recent Advances and Innovations in Engineering (ICRAIE), pp. 1-4, 2018.
6. R. Kaushik, O. P. Mahela, P. K. Bhatt, B. Khan, S. Padmanaban and F. Blaabjerg, "A Hybrid Algorithm for Recognition of Power Quality Disturbances," in IEEE Access, vol. 8, pp. 229184-229200, 2020.
7. Kaushik, R. K. "Pragati. Analysis and Case Study of Power Transmission and Distribution." J Adv Res Power Electro Power Sys 7.2 (2020): 1-3.
8. Finkelstein, A., Harman, M., Jia, Y., Martin, W., Sarro, F., Zhang, Y.: App. store analysis: Mining app. stores for relationships between customer, business and technical characteristics. Research Note of UCL Department of Computer Science 14, 10 (2014)
9. Gorla, A., Tavecchia, I., Gross, F., Zeller, A.: Checking app. behavior against app. descriptions. In: International Conference on Software Engineering, pp. 1025–1035. ACM (2014)
10. Guzman, E., Maalej, W.: How do users like this feature? A fine-grained sentiment analysis of app. reviews. In: International Conference on Requirements Engineering, pp. 153–162. IEEE (2014)
11. Hao, Y., Wang, Z., Xu, X.: Empirical study on the interface and feature evolutions of mobile apps. In: International Conference on Service-Oriented Computing, pp. 657–665. Springer (2016)
12. Iacob, C., Harrison, and R.: Retrieving and analyzing mobile apps feature requests from online reviews. In: IEEE Working Conference on Mining Software Repositories, pp. 41–44. IEEE (2013)
13. Iacob, C., Harrison, R., Faily, S.: Online reviews as first-class artifacts in mobile app. development. In: International Conference on Mobile Computing, Applications, and Services, pp. 47–53. Springer (2013)
14. Khalid, H., Shihab, E., Nagappan, M., Hassan, A.E.: What do mobile app users complain about? IEEE Soft. 32(3), 70–77 (2015)
15. Maalej, W., Nabil, H.: Bug report, feature request, or simply praise? On automatically classifying app. reviews. In: IEEE International Conference on Requirements Engineering, pp. 116–125. IEEE (2015)
16. Martin, W., Sarro, F., Harman, M.: Causal impact analysis applied to app. releases in google play and windows phone store. Research Note of UCL Department of Computer Science 15, 07 (2015)
17. Martin, W., Sarro, F., Jia, Y., Zhang, Y., Harman, and M.: A survey of app. store analysis for software engineering. IEEE Transactions on Software Engineering, PP(99), 1–32 (2016)
18. McIlroy, S., Ali, N., Hassan, and A.E.: Fresh apps: an empirical study of frequently-updated mobile apps in the google play store. Emp. Soft. Eng. 21(3), 1346–1370 (2016)
19. Sharma, Richa and Kumar, Gireesh. "Availability Modeling of Cluster-Based System with Software Aging and Optional Rejuvenation Policy" Cybernetics and Information Technologies,
20. G. Kumar and R. Sharma, "Analysis of software reliability growth model under two types of fault and warranty cost," 2017 2nd International Conference on System Reliability and Safety (ICSRS), Milan, Italy, 2017, pp. 465-468, doi: 10.1109/ICSRS.2017.8272866.
21. Kumar, G., Kaushik, M. and Purohit, R. (2018) “Reliability analysis of software with three types of errors and
imperfect debugging using Markov model,” International journal of computer applications in technology, 58(3), p.
241. doi: 10.1504/ijcat.2018.095763.
22. Gireesh, K., Manju, K. and Preeti (2016) “Maintenance policies for improving the availability of a software-hardware system,” in 2016 11th International Conference on Reliability, Maintainability and Safety (ICRMS). IEEE.
23. Sharma, R. and Kumar, G. (2017) “Availability improvement for the successive K-out-of-N machining system using standby with multiple working vacations,” International journal of reliability and safety, 11(3/4), p. 256. doi: 10.1504/ijrs.2017.089710.
24. Jain, M., Kaushik, M. and Kumar, G. (2015) “Reliability analysis for embedded system with two types of faults and common cause failure using Markov process,” in Proceedings of the Sixth International Conference on Computer and Communication Technology 2015. New York, NY, USA: ACM.
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
