Exploring Interaction Patterns for Debugging: Enhancing Conversational Capabilities of AI-assistants
<|reference_start|>Exploring Interaction Patterns for Debugging: Enhancing Conversational Capabilities of AI-assistants: The widespread availability of Large Language Models (LLMs) within Integrated Development Environments (IDEs) has led to their speedy adoption. Conversational interactions with LLMs enable programmers to obtain natural language explanations for various software development tasks. However, LLMs often leap to action without sufficient context, giving rise to implicit assumptions and inaccurate responses. Conversations between developers and LLMs are primarily structured as question-answer pairs, where the developer is responsible for asking the the right questions and sustaining conversations across multiple turns. In this paper, we draw inspiration from interaction patterns and conversation analysis -- to design Robin, an enhanced conversational AI-assistant for debugging. Through a within-subjects user study with 12 industry professionals, we find that equipping the LLM to -- (1) leverage the insert expansion interaction pattern, (2) facilitate turn-taking, and (3) utilize debugging workflows -- leads to lowered conversation barriers, effective fault localization, and 5x improvement in bug resolution rates.<|reference_end|>
@article{chopra2024exploring,
    title={Exploring Interaction Patterns for Debugging: Enhancing Conversational
  Capabilities of AI-assistants},
    author={Bhavya Chopra, Yasharth Bajpai, Param Biyani, Gustavo Soares, Arjun
  Radhakrishna, Chris Parnin, Sumit Gulwani},
    journal={arXiv preprint arXiv:2402.06229},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06229},
    primaryClass={cs.HC cs.AI cs.SE}
}