# Exploring Interaction Patterns for Debugging: Enhancing Conversational Capabilities of AI-assistants

arXiv: https://arxiv.org/abs/2402.06229 · HTML: https://mlsanity.com/paper/2402.06229/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 17 references that are in the map.
- First posted on arXiv 9 Feb 2024.

## Builds on (most cited first, 1 of 1)

- [Insert-expansions for Tool-enabled Conversational Agents](https://mlsanity.com/paper/2307.01644/) (2023)

## Built on by (most cited first, 3 of 3)

- [Human-AI Experience in Integrated Development Environments: A Systematic Literature Review](https://mlsanity.com/paper/2503.06195/) (2025)
- [VIBEPASS: Can Vibe Coders Really Pass the Vibe Check?](https://mlsanity.com/paper/2603.15921/) (2026)
- [When F1 Fails: Granularity-Aware Evaluation for Dialogue Topic Segmentation](https://mlsanity.com/paper/2512.17083/) (2025)

## Abstract (arXiv)

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.