Λεπτομέρειες βιβλιογραφικής εγγραφής
| 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] |
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| Βάση Δεδομένων: |
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