CodingGenie: A Proactive LLM-Powered Programming Assistant
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866915204251516928 |
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| author | Zhao, Sebastian Zhu, Alan Mozannar, Hussein Sontag, David Talwalkar, Ameet Chen, Valerie |
| author_facet | Zhao, Sebastian Zhu, Alan Mozannar, Hussein Sontag, David Talwalkar, Ameet Chen, Valerie |
| contents | While developers increasingly adopt tools powered by large language models (LLMs) in day-to-day workflows, these tools still require explicit user invocation. To seamlessly integrate LLM capabilities to a developer's workflow, we introduce CodingGenie, a proactive assistant integrated into the code editor. CodingGenie autonomously provides suggestions, ranging from bug fixing to unit testing, based on the current code context and allows users to customize suggestions by providing a task description and selecting what suggestions are shown. We demonstrate multiple use cases to show how proactive suggestions from CodingGenie can improve developer experience, and also analyze the cost of adding proactivity. We believe this open-source tool will enable further research into proactive assistants. CodingGenie is open-sourced at https://github.com/sebzhao/CodingGenie/ and video demos are available at https://sebzhao.github.io/CodingGenie/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14724 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CodingGenie: A Proactive LLM-Powered Programming Assistant Zhao, Sebastian Zhu, Alan Mozannar, Hussein Sontag, David Talwalkar, Ameet Chen, Valerie Human-Computer Interaction While developers increasingly adopt tools powered by large language models (LLMs) in day-to-day workflows, these tools still require explicit user invocation. To seamlessly integrate LLM capabilities to a developer's workflow, we introduce CodingGenie, a proactive assistant integrated into the code editor. CodingGenie autonomously provides suggestions, ranging from bug fixing to unit testing, based on the current code context and allows users to customize suggestions by providing a task description and selecting what suggestions are shown. We demonstrate multiple use cases to show how proactive suggestions from CodingGenie can improve developer experience, and also analyze the cost of adding proactivity. We believe this open-source tool will enable further research into proactive assistants. CodingGenie is open-sourced at https://github.com/sebzhao/CodingGenie/ and video demos are available at https://sebzhao.github.io/CodingGenie/. |
| title | CodingGenie: A Proactive LLM-Powered Programming Assistant |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2503.14724 |