AI代理能自动修复计算笔记本中的错误，提升开发效率。
Debug Smarter, Not Harder: AI Agents for Error Resolution in Computational Notebooks
- 构建可交互式探索笔记本环境的智能代理系统。
- 用户评估显示其修复能力优于传统单步工具。
- 适合数据科学家与协作开发团队使用。
计算笔记本已成为科研开发中不可或缺的工具，提供前所未有的互动性和灵活性，但同时也带来了可复现性问题和更高的出错风险。随着具备代理能力的代码流大语言模型兴起，高自主性的智能修复工具应运而生。然而，这些工具仍主要针对传统脚本编程，难以应对非线性计算笔记本的复杂结构。本文提出一种专为计算笔记本错误修复设计的AI代理系统，该系统能像用户一样与笔记本环境进行交互，并集成至 JetBrains 的协同数据科学平台 Datalore。我们通过成本对比和用户研究评估该方法，结果显示用户对代理系统的错误修复能力评分更高，但界面体验存在挑战。研究结果对优化人机协作具有重要参考价值。
原文摘要 · Abstract (English)
Computational notebooks became indispensable tools for research-related development, offering unprecedented interactivity and flexibility in the development process. However, these benefits come at the cost of reproducibility and an increased potential for bugs. With the rise of code-fluent Large Language Models empowered with agentic techniques, smart bug-fixing tools with a high level of autonomy have emerged. However, those tools are tuned for classical script programming and still struggle with non-linear computational notebooks. In this paper, we present an AI agent designed specifically for error resolution in a computational notebook. We have developed an agentic system capable of exploring a notebook environment by interacting with it -- similar to how a user would -- and integrated the system into the JetBrains service for collaborative data science called Datalore. We evaluate our approach against the pre-existing single-action solution by comparing costs and conducting a user study. Users rate the error resolution capabilities of the agentic system higher but experience difficulties with UI. We share the results of the study and consider them valuable for further improving user-agent collaboration.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。