Exploiting Vision-Language Models in GUI Reuse

Graphical user interface (GUI) prototyping helps to clarify requirements and keep stakeholders engaged in software development. While contemporary approaches retrieve GUIs relevant to a user’s query, little support exists for the actual reuse, i.e., for using an existing GUI to create a new one. To shorten the gap, we investigate GUI-centered reuse via one of the latest artificial intelligence (AI) techniques—vision-language models (VLMs). We report an empirical study involving 73 university students working on ten GUI reuse tasks. Each task is associated with different reuse directions recommended by VLMs and by a natural language (NL) method. In addition, a focused GUI element is provided to offer a starting point for making the actual changes. Our results show that VLMs significantly outperform the NL method in making reuse recommendations, but surprisingly, the focused GUI elements are not consistently modified during reuse. With the assessments made by four experienced designers, we further offer insights into the creativity of human-reuse and AI-reuse results.

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Publication Details

Published
2025-04-27
DOI
https://doi.org/10.1109/icsr66718.2025.00009
Citations
1
Primary Topic
Web Data Mining and Analysis
Type
article
Field-Weighted Citation Impact
3.25
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article

Exploiting Vision-Language Models in GUI Reuse

Jianzhang Zhang, Nan Niu, Tanmay Bhowmik, Naga Mamata Iluru et al.
1 citations
Web Data Mining and Analysis
3.25
article

Exploiting Vision-Language Models in GUI Reuse

Jianzhang Zhang, Nan Niu, Tanmay Bhowmik, Naga Mamata Iluru, Padmaja Vaishnavi Teeleti, Walaa Alshammari, Victoria Niu
article en
1 citations

Abstract

Graphical user interface (GUI) prototyping helps to clarify requirements and keep stakeholders engaged in software development. While contemporary approaches retrieve GUIs relevant to a user’s query, little support exists for the actual reuse, i.e., for using an existing GUI to create a new one. To shorten the gap, we investigate GUI-centered reuse via one of the latest artificial intelligence (AI) techniques—vision-language models (VLMs). We report an empirical study involving 73 university students working on ten GUI reuse tasks. Each task is associated with different reuse directions recommended by VLMs and by a natural language (NL) method. In addition, a focused GUI element is provided to offer a starting point for making the actual changes. Our results show that VLMs significantly outperform the NL method in making reuse recommendations, but surprisingly, the focused GUI elements are not consistently modified during reuse. With the assessments made by four experienced designers, we further offer insights into the creativity of human-reuse and AI-reuse results.

Hangzhou Normal University (CN), University of Cincinnati (US), Mississippi State University (US)
Openalex Percentile: Top 8%
Web Data Mining and Analysis
3.25
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