Authors: Bhavya Chopra, Yasharth Bajpai, Param Biyani, Gustavo Soares, Arjun Radhakrishna, Chris Parnin, Sumit Gulwani

Published on: February 09, 2024

Impact Score: 8.3

Arxiv code: Arxiv:2402.06229

Summary

• What is new: Design of Robin, an AI-assistant utilizing interaction patterns and conversation analysis for improved debugging in IDEs.
• Why this is important: LLMs in IDEs often make implicit assumptions and provide inaccurate responses due to lack of context and structured interaction.
• What the research proposes: Robin, a conversational AI-assistant leveraging interaction patterns, turn-taking, and debugging workflows for efficient debugging.
• Results: A 5x improvement in bug resolution rates among 12 industry professionals.

Technical Details

Technological frameworks used: Integrated Development Environments (IDEs) with LLM integration

Models used: Large Language Models (LLMs)

Data used: Conversational data from user study with 12 industry professionals

Potential Impact

Software development tool companies, IDE providers, and businesses investing in AI-assisted programming technologies

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