An Exploratory Study of ML Sketches and Visual Code Assistants (2412.13386v1)

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 LLMs 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.

• The paper introduces a prototype that converts developer sketches into Python code in a Jupyter Notebook using a multimodal LLM.
• The study conducted an empirical analysis among 19 data scientists, revealing that diagram sketches (52.6%) and longer sketch durations lead to improved code quality.
• The paper demonstrates the potential of bridging whiteboard design with executable code, streamlining collaborative development and innovative prototyping.

An Exploratory Study of ML Sketches and Visual Code Assistants

This paper presents an investigation into the integration of Visual Code Assistants within Integrated Development Environments (IDEs), focusing on their application in the development of ML systems. The paper addresses a significant gap in current software engineering practices where whiteboard sketches, commonly used at the initial stages of software design, lack direct mechanisms for conversion into executable code. Given the advancements in multimodal LLMs (MLLMs), which now possess capabilities to interpret both text and visual data, this research seeks to leverage these models to translate sketches into functioning code directly.

Summary of Contributions

The core contributions of this work are multifaceted. First, the authors introduce a prototype Visual Code Assistant aimed at converting developer sketches into Python code snippets within a Jupyter Notebook environment. This tool utilizes a LLM to parse and interpret sketches, generating code outlines and implementations by recognizing patterns within developer drawings. The paper involved a user experiment with 19 data scientists who regularly use sketches as part of their development workflow. This demographic is particularly relevant given its mixed praxis of creativity and technical implementation, making them ideal candidates for sketch-based coding tools.

The researchers conducted an empirical analysis of common sketching practices, discerning that diagrams are the preferred organizational tool (52.6% usage), followed by lists (42.1%) and numbered sequences (36.8%). A significant finding was the correlation between sketching duration and the quality of code generated—longer sketch times were associated with improved output quality.

The evaluation of generated code was performed using an LLM-based judgment setup, which provided a novel mechanism for assessing the code's structural and detailed accuracy. Results indicated that sketches could generate valid code outlines with 70%-80% accuracy, although the detailed implementation accuracy ranged between 25%-40%.

Implications and Future Work

This research highlights the potential for Visual Code Assistants to revolutionize how early-stage software design sketches are utilized in the development lifecycle. By transforming sketches into code, the tool both captures and preserves the transient nature of whiteboard designs, offering a persistent digital artifact that can be iteratively refined. Such functionality is particularly beneficial in collaborative settings, such as design meetings, where real-time code generation could enhance communication and idea sharing.

Moreover, the authors suggest several promising application areas for these tools, including educational contexts, prototyping, and brainstorming sessions. These domains could greatly benefit from the ability to swiftly transition from conceptual designs to tangible code, reducing the cognitive and temporal burden traditionally associated with manual coding from scratch.

Future developments should seek to refine the interactive capabilities of Visual Code Assistants, incorporating feedback mechanisms that allow developers to iteratively update both sketches and code. As LLMs continue to evolve, their integration into programming environments will likely deepen, providing even more robust support for multimodal coding tasks.

In conclusion, this paper articulates a forward-looking vision for software development where the traditional barriers between design and implementation are diminished, fostering a more fluid, visually intuitive approach to coding. By leveraging MLLMs, the authors advocate for a paradigm shift in development practices, underscoring the transformational potential of visual programming tools in the AI-assisted future of software engineering.