CodingGenie: A Proactive LLM-Powered Programming Assistant
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3696630.3728603.pdf
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1021.28 KB
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Author(s)
Zhao, Sebastian
Zhu, Alan
Mozannar, Hussein
Sontag, David
Talwalkar, Ameet
Chen, Valerie
Date Issued
July 28, 2025
Publisher
ACM|33rd ACM International Conference on the Foundations of Software Engineering
Citation
Zhao, Sebastian, Zhu, Alan, Mozannar, Hussein, Sontag, David, Talwalkar, Ameet et al. 2025. "CodingGenie: A Proactive LLM-Powered Programming Assistant."
Version
Final published version
Abstract
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/.
Description
FSE Companion ’25, June 23–28, 2025, Trondheim, Norway
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1145/3696630.3728603
