Name
Abstract | ||
|---|---|---|
Mobile app stores receive numerous reviews that contain valuable feedbacks raised by users. Incorporating user reviews into iterative delivery of new App versions would improve the quality and ratings of Apps. To date, there is no explicit answer on whether and to what degree App developers make use of user reviews sufficiently and timely. In this paper, we extract requested features in user reviews and updated features in new versions, identify the latent relation between them, and discover 7 types of Update Patterns (UPs) by grouping similar Atomic Update Units (AUs). UPs delineate common behavioral characteristics of acting on user reviews from perspectives of feature intensity trend, sufficiency and responsiveness. Statistics are conducted to explore the similarity/difference between exhibited update patterns w.r.t. Apps, features, and time. Results would help developers get a clear understanding on their own habits on how to act on user reviews, and thus offer suggestions on utilizing user reviews more efficiently in App development. |
Year | Venue | Field |
|---|---|---|
2017 | ICSOC | Mobile app,World Wide Web,App store,Computer science,Mobile deep linking,Empirical research |
DocType | Citations | PageRank |
Conference | 1 | 0.38 |
References | Authors | |
20 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shance Wang | 1 | 1 | 0.38 |
| Zhong-Jie Wang | 2 | 356 | 64.60 |
| Xiaofei Xu | 3 | 408 | 70.26 |
| Quan Z. Sheng | 4 | 3520 | 301.77 |