Demo Data from VLDB 2026 / reviewers in the wild / expert
Xiaoyun Liang 0006
dblp:362/2166
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2023
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 first-author · 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% |
Topics — the 1 heaviest of 1, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Software testing
game testing |
0.7 | 1 | 2023 | AG3: Automated Game GUI Text Glitch Detection Based on Computer Vision · ESEC/SIGSOFT FSE 2023 |
Methods — techniques the papers use, named apart from their topics
computer vision · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | AG3: Automated Game GUI Text Glitch Detection Based on Computer VisionabstractWith the advancement of device software and hardware performance, and the evolution of game engines, an increasing number of emerging high-quality games are captivating game players from all around the world who speak different languages. However, due to the vast fragmentation of the device and platform market, a well-tested game may still experience text glitches when installed on a new device with an unseen screen resolution and system version, which can significantly impact the user experience. In our testing pipeline, current testing techniques for identifying multilingual text glitches are laborious and inefficient. In this paper, we present AG3, which offers intelligent game traversal, precise visual text glitch detection, and integrated quality report generation capabilities. Our empirical evaluation and internal industrial deployment demonstrate that AG3 can detect various real-world multilingual text glitches with minimal human involvement. Xiaoyun Liang 0006, Jiayi Qi, Yongqiang Gao, Chao Peng 0002 |
ESEC/SIGSOFT FSE | 1 |