DIDUP: Dynamic Iterative Development for UI Prototyping
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866911952807133184 |
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| author | Ma, Jenny Sreedhar, Karthik Liu, Vivian Wang, Sitong Perez, Pedro Alejandro Chilton, Lydia B. |
| author_facet | Ma, Jenny Sreedhar, Karthik Liu, Vivian Wang, Sitong Perez, Pedro Alejandro Chilton, Lydia B. |
| contents | Large language models (LLMs) are remarkably good at writing code. A particularly valuable case of human-LLM collaboration is code-based UI prototyping, a method for creating interactive prototypes that allows users to view and fully engage with a user interface. We conduct a formative study of GPT Pilot, a leading LLM-generated code-prototyping system, and find that its inflexibility towards change once development has started leads to weaknesses in failure prevention and dynamic planning; it closely resembles the linear workflow of the waterfall model. We introduce DIDUP, a system for code-based UI prototyping that follows an iterative spiral model, which takes changes and iterations that come up during the development process into account. We propose three novel mechanisms for LLM-generated code-prototyping systems: (1) adaptive planning, where plans should be dynamic and reflect changes during implementation, (2) code injection, where the system should write a minimal amount of code and inject it instead of rewriting code so users have a better mental model of the code evolution, and (3) lightweight state management, a simplified version of source control so users can quickly revert to different working states. Together, this enables users to rapidly develop and iterate on prototypes. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_08474 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DIDUP: Dynamic Iterative Development for UI Prototyping Ma, Jenny Sreedhar, Karthik Liu, Vivian Wang, Sitong Perez, Pedro Alejandro Chilton, Lydia B. Human-Computer Interaction Software Engineering Large language models (LLMs) are remarkably good at writing code. A particularly valuable case of human-LLM collaboration is code-based UI prototyping, a method for creating interactive prototypes that allows users to view and fully engage with a user interface. We conduct a formative study of GPT Pilot, a leading LLM-generated code-prototyping system, and find that its inflexibility towards change once development has started leads to weaknesses in failure prevention and dynamic planning; it closely resembles the linear workflow of the waterfall model. We introduce DIDUP, a system for code-based UI prototyping that follows an iterative spiral model, which takes changes and iterations that come up during the development process into account. We propose three novel mechanisms for LLM-generated code-prototyping systems: (1) adaptive planning, where plans should be dynamic and reflect changes during implementation, (2) code injection, where the system should write a minimal amount of code and inject it instead of rewriting code so users have a better mental model of the code evolution, and (3) lightweight state management, a simplified version of source control so users can quickly revert to different working states. Together, this enables users to rapidly develop and iterate on prototypes. |
| title | DIDUP: Dynamic Iterative Development for UI Prototyping |
| topic | Human-Computer Interaction Software Engineering |
| url | https://arxiv.org/abs/2407.08474 |