Demo Data from EDBT 2026 / reviewers in the wild / expert
Zhengchang Xing
dblp:212/6791
· DBLP profile ↗
2ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Software engineering, system software, and programming languages
2 papers |
Empirical software engineering · 79% Software maintenance and evolution · 21% | |
| Human-computer interaction and pervasive computing
1 paper |
User interface design and tools · 50% Collaborative and social computing · 50% |
Topics — the 4 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Empirical software engineering › mining software repositories › app store mining
app review analysis |
0.5 | 1 | 2021 | How Should I Improve the UI of My App?: A Study of User Reviews of Popular Apps in the Google Play · ACM Trans. Softw. Eng. Methodol. 2021 |
Empirical software engineering
mining software repositories |
0.5 | 1 | 2021 | How Should I Improve the UI of My App?: A Study of User Reviews of Popular Apps in the Google Play · ACM Trans. Softw. Eng. Methodol. 2021 |
Software maintenance and evolution
feature location |
0.3 | 1 | 2017 | Reflective feature location: knowledge in mind meets information in system · Sci. China Inf. Sci. 2017 |
Empirical software engineering
developer studies |
0.1 | 1 | 2017 | Reflective feature location: knowledge in mind meets information in system · Sci. China Inf. Sci. 2017 |
Methods — techniques the papers use, named apart from their topics
statistical analysis · 1.0manual analysis · 1.0reflective practice · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | How Should I Improve the UI of My App?: A Study of User Reviews of Popular Apps in the Google PlayabstractUI (User Interface) is an essential factor influencing users’ perception of an app. However, it is hard for even professional designers to determine if the UI is good or not for end-users. Users’ feedback (e.g., user reviews in the Google Play) provides a way for app owners to understand how the users perceive the UI. In this article, we conduct an in-depth empirical study to analyze the UI issues of mobile apps. In particular, we analyze more than 3M UI-related reviews from 22,199 top free-to-download apps and 9,380 top non-free apps in the Google Play Store. By comparing the rating of UI-related reviews and other reviews of an app, we observe that UI-related reviews have lower ratings than other reviews. By manually analyzing a random sample of 1,447 UI-related reviews with a 95% confidence level and a 5% interval, we identify 17 UI-related issues types that belong to four categories (i.e., “Appearance,” “Interaction,” “Experience,” and “Others” ). In these issue types, we find “Generic Review” is the most occurring one. “Comparative Review” and “Advertisement” are the most negative two UI issue types. Faced with these UI issues, we explore the patterns of interaction between app owners and users. We identify eight patterns of how app owners dialogue with users about UI issues by the review-response mechanism. We find “Apology or Appreciation” and “Information Request” are the most two frequent patterns. We find updating UI timely according to feedback is essential to satisfy users. Besides, app owners could also fix UI issues without updating UI, especially for issue types belonging to “Interaction” category. Our findings show that there exists a positive impact if app owners could actively interact with users to improve UI quality and boost users’ satisfactoriness about the UIs. Qiuyuan Chen, Chunyang Chen 0001, Safwat Hassan, Zhengchang Xing, Xin Xia 0001, Ahmed E. Hassan |
ACM Trans. Softw. Eng. Methodol. | 4 |
| 2017 | Reflective feature location: knowledge in mind meets information in system
Xin Peng 0001, Zhengchang Xing, Sen Pan, Wenyi Qian, Václav Rajlich, Wenyun Zhao |
Sci. China Inf. Sci. | 2 |