DroidBot-GPT: GPT-powered UI Automation for Android
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866916082600640512 |
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| author | Wen, Hao Wang, Hongming Liu, Jiaxuan Li, Yuanchun |
| author_facet | Wen, Hao Wang, Hongming Liu, Jiaxuan Li, Yuanchun |
| contents | This paper introduces DroidBot-GPT, a tool that utilizes GPT-like large language models (LLMs) to automate the interactions with Android mobile applications. Given a natural language description of a desired task, DroidBot-GPT can automatically generate and execute actions that navigate the app to complete the task. It works by translating the app GUI state information and the available actions on the smartphone screen to natural language prompts and asking the LLM to make a choice of actions. Since the LLM is typically trained on a large amount of data including the how-to manuals of diverse software applications, it has the ability to make reasonable choices of actions based on the provided information. We evaluate DroidBot-GPT with a self-created dataset that contains 33 tasks collected from 17 Android applications spanning 10 categories. It can successfully complete 39.39% of the tasks, and the average partial completion progress is about 66.76%. Given the fact that our method is fully unsupervised (no modification required from both the app and the LLM), we believe there is great potential to enhance automation performance with better app development paradigms and/or custom model training. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_07061 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | DroidBot-GPT: GPT-powered UI Automation for Android Wen, Hao Wang, Hongming Liu, Jiaxuan Li, Yuanchun Software Engineering Artificial Intelligence This paper introduces DroidBot-GPT, a tool that utilizes GPT-like large language models (LLMs) to automate the interactions with Android mobile applications. Given a natural language description of a desired task, DroidBot-GPT can automatically generate and execute actions that navigate the app to complete the task. It works by translating the app GUI state information and the available actions on the smartphone screen to natural language prompts and asking the LLM to make a choice of actions. Since the LLM is typically trained on a large amount of data including the how-to manuals of diverse software applications, it has the ability to make reasonable choices of actions based on the provided information. We evaluate DroidBot-GPT with a self-created dataset that contains 33 tasks collected from 17 Android applications spanning 10 categories. It can successfully complete 39.39% of the tasks, and the average partial completion progress is about 66.76%. Given the fact that our method is fully unsupervised (no modification required from both the app and the LLM), we believe there is great potential to enhance automation performance with better app development paradigms and/or custom model training. |
| title | DroidBot-GPT: GPT-powered UI Automation for Android |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2304.07061 |