Demo Data from EDBT 2026 / reviewers in the wild / expert
Zhibin Wei
dblp:244/6372
· DBLP profile ↗
3ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Software engineering, systems software and programming languages · 1Databases, data management, data science and information retrieval · 1 · 1 since 2021
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
1 paper |
Software testing · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Collaborative and social computing · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Collaborative and social computing
collective intelligence |
0.4 | 1 | 2019 | CoCoTest: collaborative crowdsourced testing for Android applications · ISSTA 2019 |
Software testing › dynamic testing › manual testing
crowdsourced testing |
0.4 | 1 | 2019 | CoCoTest: collaborative crowdsourced testing for Android applications · ISSTA 2019 |
Software testing
mobile application testing |
0.4 | 1 | 2019 | CoCoTest: collaborative crowdsourced testing for Android applications · ISSTA 2019 |
Methods — techniques the papers use, named apart from their topics
duplicate detection · 0.8bug report aggregation · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Bayesian contrastive Learning: An augmentation-free fault diagnosis method with limited labels and uncertainty quantification
Hassaan Ahmad, Wei Cheng 0007, Zhibin Wei, Shuo Zhang 0017, Zelin Nie, Jiangkun Yang, Xuefeng Chen 0002 |
Adv. Eng. Informatics | 5 |
| 2020 | Research on Correlation Between Underground Squares' Interface Morphology and Spatial Experience Based on Virtual RealityabstractUnderground square, as a recreation and activity place for citizens, represents the underground space quality of a city, so its spatial experience is of great significance. This research discussed the influence and effects of underground squares’ interface morphology on spatial experience. By field research of ten large-scale urban underground spaces, the basic elements and related numerical interval of underground squares’ interface morphology were summarized, and on basis of this, underground square virtual models with different interface morphology were built. Based on the platform of immersive virtual reality system (IVRS), combing the isovist method and the SD method, this paper made a quantitative analysis on the relevance between interface morphology and spatial experience, and the relevant compliance indicators of interface morphology were summarized. The experimental results show that the spatial experience is good at the interface density of 0.402 and interface opening’s aspect ratio of 2–3. This can provide reference and foundation for urban underground spatial design in future. Le Feng, Han Wong, Zhibin Wei |
Int. J. Pattern Recognit. Artif. Intell. | 6 |
| 2019 | CoCoTest: collaborative crowdsourced testing for Android applicationsabstractTesting Android applications is becoming more and more challenging due to the notorious fragmentation issues and the complexity of usage scenarios in different environments. Crowdsourced testing has grown as a trend, especially in mobile application testing. However, due to the lack of professionalism and communication, the crowd workers tend to submit low-quality and duplicate bug reports, leading to a waste of test resources on inspecting and aggregating such reports. To solve these problems, we developed a platform, CoCoTest, embracing the idea of collective intelligence. With the help of CoCoTest Android SDK, workers can efficiently capture a screenshot, write a short description and create a bug report. A series of bug reports are aggregated online and then recommended to the other workers in real time. The crowdsourced workers can (1) help review, verify and enrich each others' bug reports; (2) escape duplicate bug reports; (3) be guided to conduct more professional testing with the help of collective intelligence. CoCoTest can improve the quality of the final report and reduce test costs. The demo video can be found at https://youtu.be/PuVuPbNP4tY. Chunrong Fang, Zhibin Wei, Zhenyu Chen 0001 |
ISSTA | 3 |