Academic Journal
ViMSUIT: Vision-Metadata Inferences for Software UI Testing.
| Τίτλος: | ViMSUIT: Vision-Metadata Inferences for Software UI Testing. |
|---|---|
| Συγγραφείς: | Kumari, Pragati, Dey, Arnab, Deb, Novarun |
| Πηγή: | Innovations in Systems & Software Engineering; Dec2026, Vol. 22 Issue 2, p1-13, 13p |
| Περίληψη: | Automated mobile user interface (UI) testing remains a challenge due to the high dependency on platform-specific metadata and the manual effort required to author robust test scripts. We present ViMSUIT, an autonomous testing framework that bridges the gap between visual intent and executable script generation by leveraging the synergistic reasoning of Vision Language Models (VLMs) and Large Language Models (LLMs). Unlike traditional approaches, ViMSUIT utilizes a VLM to synthesize structured natural language descriptions directly from application screenshots, which are then transformed into functionally grounded Espresso test scripts. Our evaluation, conducted on a diverse sample of 200 screens from the ENRICO dataset, demonstrates that ViMSUIT achieves a test case correctness rate of 91.46% as validated by human experts. Furthermore, the framework effectively filters computational noise in UI metadata, achieving an average functional element coverage of 74.62%, with full coverage being achieved in 49.4% of the evaluated screens. These results indicate that ViMSUIT provides a scalable, metadata-independent solution for generating semantically accurate and executable mobile test suites, significantly reducing the barrier to autonomous quality assurance in mobile development. [ABSTRACT FROM AUTHOR] |
| Copyright of Innovations in Systems & Software Engineering is the property of Springer Nature 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: ViMSUIT: Vision-Metadata Inferences for Software UI Testing. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kumari%2C+Pragati%22">Kumari, Pragati</searchLink><br /><searchLink fieldCode="AR" term="%22Dey%2C+Arnab%22">Dey, Arnab</searchLink><br /><searchLink fieldCode="AR" term="%22Deb%2C+Novarun%22">Deb, Novarun</searchLink> – Name: TitleSource Label: Source Group: Src Data: Innovations in Systems & Software Engineering; Dec2026, Vol. 22 Issue 2, p1-13, 13p – Name: Abstract Label: Abstract Group: Ab Data: Automated mobile user interface (UI) testing remains a challenge due to the high dependency on platform-specific metadata and the manual effort required to author robust test scripts. We present ViMSUIT, an autonomous testing framework that bridges the gap between visual intent and executable script generation by leveraging the synergistic reasoning of Vision Language Models (VLMs) and Large Language Models (LLMs). Unlike traditional approaches, ViMSUIT utilizes a VLM to synthesize structured natural language descriptions directly from application screenshots, which are then transformed into functionally grounded Espresso test scripts. Our evaluation, conducted on a diverse sample of 200 screens from the ENRICO dataset, demonstrates that ViMSUIT achieves a test case correctness rate of 91.46% as validated by human experts. Furthermore, the framework effectively filters computational noise in UI metadata, achieving an average functional element coverage of 74.62%, with full coverage being achieved in 49.4% of the evaluated screens. These results indicate that ViMSUIT provides a scalable, metadata-independent solution for generating semantically accurate and executable mobile test suites, significantly reducing the barrier to autonomous quality assurance in mobile development. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Innovations in Systems & Software Engineering is the property of Springer Nature 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: 10.1007/s11334-026-00656-2 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1 Titles: – TitleFull: ViMSUIT: Vision-Metadata Inferences for Software UI Testing. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kumari, Pragati – PersonEntity: Name: NameFull: Dey, Arnab – PersonEntity: Name: NameFull: Deb, Novarun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16145046 Numbering: – Type: volume Value: 22 – Type: issue Value: 2 Titles: – TitleFull: Innovations in Systems & Software Engineering Type: main |
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