Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Xiaoyun Liang 0006

dblp:362/2166 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Software testing
game testing
0.712023
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
YearPublicationVenuePosition
2023 AG3: Automated Game GUI Text Glitch Detection Based on Computer Vision
abstract
With 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 FSE1