Publication Type
Conference Proceeding Article
Version
submittedVersion
Publication Date
5-2025
Abstract
The 2025 ACM Web Conference (WWW '25) took place from April 28 to May 2, 2025, in the Sydney Convention & Exhibition Centre, Australia. Its logo, featuring the Sydney Harbour Bridge, symbolizes the core "connecting" function of the Web. Formerly known as the International World Wide Web Conference (WWW), this event originated at CERN in 1994 and has long served as the premier venue for presenting and discussing research, development, standards, and applications related to the Web.The 2025 ACM Web Conference (WWW'25) took place from April 28 to May 2, 2025, in the Sydney Convention & Exhibition Centre, Australia. Its logo, featuring the Sydney Harbour Bridge, symbolizes the core "connecting" function of the Web. Formerly known as the International World Wide Web Conference (WWW), this event originated at CERN in 1994 and has long served as the premier venue for presenting and discussing research, development, standards, and applications related to the Web.Between 2024 and 2025, large language models (LLMs) significantly impacted nearly every industry and many aspects of daily life, prompting transformations in the Web's ecosystem. Acknowledging the importance of LLMs in advancing Web technologies, the call for papers (CFP) across ten research tracks was slightly modified. Three program chairs - Liane Lewin-Eytan, Helen Huang, and Elad Yom-Tov - led the program committee, which used OpenReview to evaluate and accept the research track papers.
Keywords
Web-based Deep Learning, Fuzzing, Large Language Model
Discipline
Programming Languages and Compilers | Software Engineering
Research Areas
Intelligent Systems and Optimization
Areas of Excellence
Digital transformation
Publication
WWW '25: Proceedings of the ACM on Web Conference 2025, Sydney, Australia, April 28 - May 2
First Page
3405
Last Page
3414
ISBN
9798400712746
Identifier
10.1145/3696410.3714649
Publisher
ACM
City or Country
New York
Citation
QUAN, Lili; XIE, Xiaofei; GUO, Qianyu; JIANG, Lingxiao; CHEN, Sen; WANG, Junjie; and LI, Xiaohong.
TensorJSFuzz: Effective testing of web-based deep learning frameworks via input-constraint extraction. (2025). WWW '25: Proceedings of the ACM on Web Conference 2025, Sydney, Australia, April 28 - May 2. 3405-3414.
Available at: https://ink.library.smu.edu.sg/sis_research/10326
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/3696410.3714649