CodingGenie: A Proactive LLM-Powered Programming Assistant
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/.
Authors
- Hussein Mozannar (ORCID: https://orcid.org/0009-0001-9660-9878)
- David Sontag (ORCID: https://orcid.org/0000-0002-5034-7796)
- Ameet Talwalkar (ORCID: https://orcid.org/0000-0001-6650-1893)
- Alan Zhu (ORCID: https://orcid.org/0009-0000-5737-7923)
- Valerie Chen (ORCID: https://orcid.org/0009-0007-2783-0265)
- S Zhao (ORCID: https://orcid.org/0009-0002-3419-1231)
Institutions
- Microsoft (United States) (US)
- IIT@MIT (US)
- Carnegie Mellon University (US)
- University of California, Berkeley (US)
Publication Details
- Published
- 2025-06-23
- DOI
- https://doi.org/10.1145/3696630.3728603
- Citations
- 3
- Primary Topic
- Parallel Computing and Optimization Techniques
- Type
- article
- Field-Weighted Citation Impact
- 6.81