Xinyue Liu (刘心悦)
I’m currently an Ph.D. student advised by Lukasz Ziarek at University at Buffalo, Department of Computer Science and Engineering. My research interest focus on software engineering and programming language. More specifically, I’m working on automated testing and detection of web applications. Besides, I am also interested in the validation and application of a new web language — WebAssembly — the future of the web.
You can contact me through the email: xliu234@buffalo.edu.
A printable CV of mine can be found here.
Education
- 2016 Fall - 2020 Summer: Nanjing University (NJU 南京大学), Bachelor
- 2021 Spring - Current: University at Buffalo (UB), Ph.D. candidate
Experience
2016 Fall - 2018 Summer: Minister of NJU Phantom Magic Club
2016 Fall - 2018 Summer: Captain of NJU Volleyball team
2016 Fall - 2018 Summer: NJU student union member
2019 Summer: Intern gameplay developer at Tencent Timi J5 Studio
2021 Spring - 2022 Fall: Teaching assistant of UB CSE 531: Algorithm Analysis and Design, Prof. Xin (Roger) He
Publications
X. Liu, L. Ziarek, “PTV: Better Version Detection on JavaScript Web Library Based on Unique Subtree Mining,” 47th International Conference on Software Engineering (ICSE 2025). [pdf1] [review1] [rebuttal1] [pdf2]
X. Liu, Z. Song, W. Fang, W. Yang, W. Wang, “WEFix: Intelligent Automatic Generation of Explicit Waits for Efficient Web End-to-End Flaky Tests,” The Web Conference 2024 (WWW 2024). (Acceptance: 20.2%, 405/2008) [pdf] [review]
X. Liu, L. Ziarek, “PTdetector: An Automated JavaScript Front-end Library Detector,” 38th IEEE/ACM International Conference on Automated Software Engineering (ASE 2023). (Acceptance: 21%, 103/629) [pdf] [review]
Y. Yan, Y. Zheng, X. Liu, N. Medvidovic, W. Wang, “AdHere: Automated Detection and Repair of Intrusive Ads,” 45th IEEE/ACM International Conference on Software Engineering (ICSE 2023). (Acceptance: 26%, 209/796) [pdf]
A. Romano, X. Liu, Y. Kwon, W. Wang, “An Empirical Study of Bugs in WebAssembly Compilers,” 36th IEEE/ACM International Conference on Automated Software Engineering (ASE 2021). (Acceptance: 19.2%, 82/427) [pdf]
X. Liu, Yanhui Li , “Is Bigger Data Better for Defect Prediction: Examining the Impact of Data Size on Supervised and Unsupervised Defect Prediction,” 12 pages, Sep. 2019, WISA 2019