A Siamese Network-Based Visual Structure Comparison Approach for Addressing the Dynamic Content Challenge in Web Visual Testing.
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| Title: | A Siamese Network-Based Visual Structure Comparison Approach for Addressing the Dynamic Content Challenge in Web Visual Testing. |
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| Authors: | LIU, JHONG-YUN1 john40066@gmail.com, LIN, TI-MO1 lintimo1999@gmail.com, LEE, SHIN-JIE1 jielee@mail.ncku.edu.tw, ZHANG, CI-YIN1 solemn0219@gmail.com, CHU, WEI-TA1 wtchu@gs.ncku.edu.tw |
| Source: | Journal of Information Science & Engineering. May2026, Vol. 42 Issue 3, p615-628. 14p. |
| Subjects: | False positive error, Computer software testing, Internet content, Artificial neural networks |
| Abstract: | Visual testing for cross-browser testing is an essential part of web automated regression testing. However, there is an inevitable challenge in visual testing, which is "dynamic content." Dynamic content, such as advertisements and news, constantly changes its appearance over and over time. Existing visual testing tools fail to handle dynamic content, and some even treat it as cross-browser incompatibilities (XBIs), then generate false positives warning to developer. Currently there are two types of approaches to identifying dynamic content First, manually marking the positions of dynamic content does not significantly alleviate the developers' workload. Second, repeatedly executing the website on the same browser and regarding different pixels as dynamic content However, this method can lead to false judgments. In this paper, we propose a Siamese network-based comparison approach to detect visual disparities by comparing the visual structures of a webpage rendered by two browsers. This approach can identify cross-browser inconsistencies and accommodate minor element shifts, achieving a high F1-score of 0.9749. A comparison with the usage of structural similarity (SSIM) and multi-scale structural similarity (MSSSIM) is also carried out and discussed. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Information Science & Engineering is the property of Institute of Information Science, Academia Sinica and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 193961060 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Siamese Network-Based Visual Structure Comparison Approach for Addressing the Dynamic Content Challenge in Web Visual Testing. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22LIU%2C+JHONG-YUN%22">LIU, JHONG-YUN</searchLink><relatesTo>1</relatesTo><i> john40066@gmail.com</i><br /><searchLink fieldCode="AR" term="%22LIN%2C+TI-MO%22">LIN, TI-MO</searchLink><relatesTo>1</relatesTo><i> lintimo1999@gmail.com</i><br /><searchLink fieldCode="AR" term="%22LEE%2C+SHIN-JIE%22">LEE, SHIN-JIE</searchLink><relatesTo>1</relatesTo><i> jielee@mail.ncku.edu.tw</i><br /><searchLink fieldCode="AR" term="%22ZHANG%2C+CI-YIN%22">ZHANG, CI-YIN</searchLink><relatesTo>1</relatesTo><i> solemn0219@gmail.com</i><br /><searchLink fieldCode="AR" term="%22CHU%2C+WEI-TA%22">CHU, WEI-TA</searchLink><relatesTo>1</relatesTo><i> wtchu@gs.ncku.edu.tw</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Information+Science+%26+Engineering%22">Journal of Information Science & Engineering</searchLink>. May2026, Vol. 42 Issue 3, p615-628. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22False+positive+error%22">False positive error</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+testing%22">Computer software testing</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+content%22">Internet content</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Visual testing for cross-browser testing is an essential part of web automated regression testing. However, there is an inevitable challenge in visual testing, which is "dynamic content." Dynamic content, such as advertisements and news, constantly changes its appearance over and over time. Existing visual testing tools fail to handle dynamic content, and some even treat it as cross-browser incompatibilities (XBIs), then generate false positives warning to developer. Currently there are two types of approaches to identifying dynamic content First, manually marking the positions of dynamic content does not significantly alleviate the developers' workload. Second, repeatedly executing the website on the same browser and regarding different pixels as dynamic content However, this method can lead to false judgments. In this paper, we propose a Siamese network-based comparison approach to detect visual disparities by comparing the visual structures of a webpage rendered by two browsers. This approach can identify cross-browser inconsistencies and accommodate minor element shifts, achieving a high F1-score of 0.9749. A comparison with the usage of structural similarity (SSIM) and multi-scale structural similarity (MSSSIM) is also carried out and discussed. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Information Science & Engineering is the property of Institute of Information Science, Academia Sinica and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.6688/JISE.202605_42(3).0008 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 615 Subjects: – SubjectFull: False positive error Type: general – SubjectFull: Computer software testing Type: general – SubjectFull: Internet content Type: general – SubjectFull: Artificial neural networks Type: general Titles: – TitleFull: A Siamese Network-Based Visual Structure Comparison Approach for Addressing the Dynamic Content Challenge in Web Visual Testing. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: LIU, JHONG-YUN – PersonEntity: Name: NameFull: LIN, TI-MO – PersonEntity: Name: NameFull: LEE, SHIN-JIE – PersonEntity: Name: NameFull: ZHANG, CI-YIN – PersonEntity: Name: NameFull: CHU, WEI-TA IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10162364 Numbering: – Type: volume Value: 42 – Type: issue Value: 3 Titles: – TitleFull: Journal of Information Science & Engineering Type: main |
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