Exploring Interaction Patterns for Debugging: Enhancing Conversational Capabilities of AI-assistants
This addresses the issue for programmers using AI-assistants in software development by improving conversational debugging capabilities, though it is incremental as it builds on existing interaction patterns.
The paper tackled the problem of LLMs in IDEs making implicit assumptions and giving inaccurate responses due to insufficient context, by designing Robin, an enhanced conversational AI-assistant for debugging, which resulted in lowered conversation barriers, effective fault localization, and a 5x improvement in bug resolution rates.
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.