Utilizing Machine Learning for the Identification of Visually Similar Web Elements

Yi Ju Cho; Shin Jie Lee

doi:10.1109/ICEBE59045.2023.00044

Utilizing Machine Learning for the Identification of Visually Similar Web Elements

Yi Ju Cho
, Shin Jie Lee

Computer and Network Center

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

A significant proportion of web elements might manifest identical behaviors and functionalities, further complicating the test case creation process. We have noted that elements demonstrating congruent behavior and functionality frequently exhibit visual resemblances within the web page interface. In this paper, we propose a machine learning-based method to identify visually similar web elements. These identified elements can be brought to the attention of the tester. If the tester determines that these elements exhibit similar behavior, they can be visually marked on the web page, facilitating focused test case creation. A case study demonstrates its potential to make accurate judgments on several complex webpages.

Original language	English
Title of host publication	Proceedings - 2023 IEEE International Conference on e-Business Engineering, ICEBE 2023
Editors	Omar Khadeer Hussain, Shang-Pin Ma, Xin Lu, Kuo-Ming Chao
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	181-186
Number of pages	6
ISBN (Electronic)	9798350325553
DOIs	https://doi.org/10.1109/ICEBE59045.2023.00044
Publication status	Published - 2023
Event	19th IEEE International Conference on e-Business Engineering, ICEBE 2023 - Sydney, Australia Duration: 2023 Nov 4 → 2023 Nov 6

Publication series

Name	Proceedings - 2023 IEEE International Conference on e-Business Engineering, ICEBE 2023

Conference

Conference	19th IEEE International Conference on e-Business Engineering, ICEBE 2023
Country/Territory	Australia
City	Sydney
Period	23-11-04 → 23-11-06

All Science Journal Classification (ASJC) codes

Business, Management and Accounting (miscellaneous)
Artificial Intelligence
Computer Networks and Communications
Information Systems
Information Systems and Management
Safety, Risk, Reliability and Quality

Access to Document

10.1109/ICEBE59045.2023.00044

Cite this

Cho, Y. J., & Lee, S. J. (2023). Utilizing Machine Learning for the Identification of Visually Similar Web Elements. In O. K. Hussain, S.-P. Ma, X. Lu, & K.-M. Chao (Eds.), Proceedings - 2023 IEEE International Conference on e-Business Engineering, ICEBE 2023 (pp. 181-186). (Proceedings - 2023 IEEE International Conference on e-Business Engineering, ICEBE 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICEBE59045.2023.00044

Cho, Yi Ju ; Lee, Shin Jie. / Utilizing Machine Learning for the Identification of Visually Similar Web Elements. Proceedings - 2023 IEEE International Conference on e-Business Engineering, ICEBE 2023. editor / Omar Khadeer Hussain ; Shang-Pin Ma ; Xin Lu ; Kuo-Ming Chao. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 181-186 (Proceedings - 2023 IEEE International Conference on e-Business Engineering, ICEBE 2023).

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title = "Utilizing Machine Learning for the Identification of Visually Similar Web Elements",

abstract = "A significant proportion of web elements might manifest identical behaviors and functionalities, further complicating the test case creation process. We have noted that elements demonstrating congruent behavior and functionality frequently exhibit visual resemblances within the web page interface. In this paper, we propose a machine learning-based method to identify visually similar web elements. These identified elements can be brought to the attention of the tester. If the tester determines that these elements exhibit similar behavior, they can be visually marked on the web page, facilitating focused test case creation. A case study demonstrates its potential to make accurate judgments on several complex webpages.",

author = "Cho, \{Yi Ju\} and Lee, \{Shin Jie\}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 19th IEEE International Conference on e-Business Engineering, ICEBE 2023 ; Conference date: 04-11-2023 Through 06-11-2023",

year = "2023",

doi = "10.1109/ICEBE59045.2023.00044",

language = "English",

series = "Proceedings - 2023 IEEE International Conference on e-Business Engineering, ICEBE 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "181--186",

editor = "Hussain, \{Omar Khadeer\} and Shang-Pin Ma and Xin Lu and Kuo-Ming Chao",

booktitle = "Proceedings - 2023 IEEE International Conference on e-Business Engineering, ICEBE 2023",

address = "United States",

}

Cho, YJ & Lee, SJ 2023, Utilizing Machine Learning for the Identification of Visually Similar Web Elements. in OK Hussain, S-P Ma, X Lu & K-M Chao (eds), Proceedings - 2023 IEEE International Conference on e-Business Engineering, ICEBE 2023. Proceedings - 2023 IEEE International Conference on e-Business Engineering, ICEBE 2023, Institute of Electrical and Electronics Engineers Inc., pp. 181-186, 19th IEEE International Conference on e-Business Engineering, ICEBE 2023, Sydney, Australia, 23-11-04. https://doi.org/10.1109/ICEBE59045.2023.00044

Utilizing Machine Learning for the Identification of Visually Similar Web Elements. / Cho, Yi Ju; Lee, Shin Jie.
Proceedings - 2023 IEEE International Conference on e-Business Engineering, ICEBE 2023. ed. / Omar Khadeer Hussain; Shang-Pin Ma; Xin Lu; Kuo-Ming Chao. Institute of Electrical and Electronics Engineers Inc., 2023. p. 181-186 (Proceedings - 2023 IEEE International Conference on e-Business Engineering, ICEBE 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - A significant proportion of web elements might manifest identical behaviors and functionalities, further complicating the test case creation process. We have noted that elements demonstrating congruent behavior and functionality frequently exhibit visual resemblances within the web page interface. In this paper, we propose a machine learning-based method to identify visually similar web elements. These identified elements can be brought to the attention of the tester. If the tester determines that these elements exhibit similar behavior, they can be visually marked on the web page, facilitating focused test case creation. A case study demonstrates its potential to make accurate judgments on several complex webpages.

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M3 - Conference contribution

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Cho YJ, Lee SJ. Utilizing Machine Learning for the Identification of Visually Similar Web Elements. In Hussain OK, Ma SP, Lu X, Chao KM, editors, Proceedings - 2023 IEEE International Conference on e-Business Engineering, ICEBE 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 181-186. (Proceedings - 2023 IEEE International Conference on e-Business Engineering, ICEBE 2023). doi: 10.1109/ICEBE59045.2023.00044

Utilizing Machine Learning for the Identification of Visually Similar Web Elements

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All Science Journal Classification (ASJC) codes

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