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An Exploratory Study of ML Sketches and Visual Code Assistants

2024-12-17 23:44:45
Lu\'is F. Gomes, Vincent J. Hellendoorn, Jonathan Aldrich, Rui Abreu

Abstract

This paper explores the integration of Visual Code Assistants in Integrated Development Environments (IDEs). In Software Engineering, whiteboard sketching is often the initial step before coding, serving as a crucial collaboration tool for developers. Previous studies have investigated patterns in SE sketches and how they are used in practice, yet methods for directly using these sketches for code generation remain limited. The emergence of visually-equipped large language models presents an opportunity to bridge this gap, which is the focus of our research. In this paper, we built a first prototype of a Visual Code Assistant to get user feedback regarding in-IDE sketch-to-code tools. We conduct an experiment with 19 data scientists, most of whom regularly sketch as part of their job. We investigate developers' mental models by analyzing patterns commonly observed in their sketches when developing an ML workflow. Analysis indicates that diagrams were the preferred organizational component (52.6%), often accompanied by lists (42.1%) and numbered points (36.8%). Our tool converts their sketches into a Python notebook by querying an LLM. We use an LLM-as-judge setup to score the quality of the generated code, finding that even brief sketching can effectively generate useful code outlines. We also find a positive correlation between sketch time and the quality of the generated code. We conclude the study by conducting extensive interviews to assess the tool's usefulness, explore potential use cases, and understand developers' needs. As noted by participants, promising applications for these assistants include education, prototyping, and collaborative settings. Our findings signal promise for the next generation of Code Assistants to integrate visual information, both to improve code generation and to better leverage developers' existing sketching practices.

Abstract (translated)

本文探讨了在集成开发环境(IDE)中整合视觉代码助手的可能性。在软件工程中,白板草图往往是编码前的第一步,作为开发者之间的重要协作工具。先前的研究已经调查了SE草图中的模式及其实际应用情况,但直接利用这些草图进行代码生成的方法仍然有限。配备视觉功能的大语言模型的出现为填补这一空白提供了机会,这也是我们研究的重点所在。在本文中,我们构建了一个可视代码助手的第一个原型,以获取用户对IDE内草图转代码工具的反馈。我们进行了一个实验,对象是19位数据科学家,其中大多数人在工作中经常进行草图绘制。我们通过分析开发ML工作流时常见于他们草图中的模式来研究开发者们的心理模型。分析表明,图表是最受欢迎的组织组件(52.6%),通常伴随列表(42.1%)和编号要点(36.8%)。我们的工具通过查询大语言模型将他们的草图转换成Python笔记本。我们使用大语言模型作为评分者来评估生成代码的质量,发现即使是简短的草图也能有效地生成有用的代码大纲。我们还发现了草图时间和生成代码质量之间的正相关关系。我们通过对参与者进行广泛的访谈以评估工具的实用性、探索潜在应用场景以及理解开发者的具体需求而结束了这项研究。正如参与者所指出的那样,这些助手在教育、原型设计和协作场景中的应用前景广阔。我们的发现表明,下一代代码助手整合视觉信息具有潜力,既能够提高代码生成的质量,也能更好地利用开发者现有的草图绘制习惯。

URL

https://arxiv.org/abs/2412.13386

PDF

https://arxiv.org/pdf/2412.13386.pdf


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