CoPrompt: Supporting Prompt Sharing and Referring in Collaborative Natural Language Programming
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866910489576996864 |
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| author | Feng, Li Yen, Ryan You, Yuzhe Fan, Mingming Zhao, Jian Lu, Zhicong |
| author_facet | Feng, Li Yen, Ryan You, Yuzhe Fan, Mingming Zhao, Jian Lu, Zhicong |
| contents | Natural language (NL) programming has become more approachable due to the powerful code-generation capability of large language models (LLMs). This shift to using NL to program enhances collaborative programming by reducing communication barriers and context-switching among programmers from varying backgrounds. However, programmers may face challenges during prompt engineering in a collaborative setting as they need to actively keep aware of their collaborators' progress and intents. In this paper, we aim to investigate ways to assist programmers' prompt engineering in a collaborative context. We first conducted a formative study to understand the workflows and challenges of programmers when using NL for collaborative programming. Based on our findings, we implemented a prototype, CoPrompt, to support collaborative prompt engineering by providing referring, requesting, sharing, and linking mechanisms. Our user study indicates that CoPrompt assists programmers in comprehending collaborators' prompts and building on their collaborators' work, reducing repetitive updates and communication costs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_09235 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | CoPrompt: Supporting Prompt Sharing and Referring in Collaborative Natural Language Programming Feng, Li Yen, Ryan You, Yuzhe Fan, Mingming Zhao, Jian Lu, Zhicong Human-Computer Interaction Natural language (NL) programming has become more approachable due to the powerful code-generation capability of large language models (LLMs). This shift to using NL to program enhances collaborative programming by reducing communication barriers and context-switching among programmers from varying backgrounds. However, programmers may face challenges during prompt engineering in a collaborative setting as they need to actively keep aware of their collaborators' progress and intents. In this paper, we aim to investigate ways to assist programmers' prompt engineering in a collaborative context. We first conducted a formative study to understand the workflows and challenges of programmers when using NL for collaborative programming. Based on our findings, we implemented a prototype, CoPrompt, to support collaborative prompt engineering by providing referring, requesting, sharing, and linking mechanisms. Our user study indicates that CoPrompt assists programmers in comprehending collaborators' prompts and building on their collaborators' work, reducing repetitive updates and communication costs. |
| title | CoPrompt: Supporting Prompt Sharing and Referring in Collaborative Natural Language Programming |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2310.09235 |