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Natural language (NL) programming has become more approachable due to thepowerful code-generation capability of large language models (LLMs). This shiftto using NL to program enhances collaborative programming by reducingcommunication barriers and context-switching among programmers from varyingbackgrounds. However, programmers may face challenges during prompt engineeringin a collaborative setting as they need to actively keep aware of theircollaborators' progress and intents. In this paper, we aim to investigate waysto assist programmers' prompt engineering in a collaborative context. We firstconducted a formative study to understand the workflows and challenges ofprogrammers when using NL for collaborative programming. Based on our findings,we implemented a prototype, CoPrompt, to support collaborative promptengineering by providing referring, requesting, sharing, and linkingmechanisms. Our user study indicates that CoPrompt assists programmers incomprehending collaborators' prompts and building on their collaborators' work,reducing repetitive updates and communication costs.

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