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arXiv · 2310.09235

CoPrompt: Supporting Prompt Sharing and Referring in Collaborative Natural Language Programming

Li Feng·Ryan Yen·Yuzhe You·Mingming Fan·Jian Zhao·Zhicong Lu
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Abstract

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.

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Li Feng, Ryan Yen, Yuzhe You, Mingming Fan, Jian Zhao, Zhicong Lu. 2023-10-13. CoPrompt: Supporting Prompt Sharing and Referring in Collaborative Natural Language Programming. https://doi.org/10.1145/3613904.3642212

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