# Documentation Matters: Human-Centered AI System to Assist Data Science Code Documentation in Computational Notebooks

arXiv: https://arxiv.org/abs/2102.12592 · HTML: https://mlsanity.com/paper/2102.12592/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2021.

## Built on by (most cited first, 17 of 17)

- [Natural Language to Code Generation in Interactive Data Science Notebooks](https://mlsanity.com/paper/2212.09248/) (2022)
- [Can LLMs Generate Architectural Design Decisions? -An Exploratory Empirical study](https://mlsanity.com/paper/2403.01709/) (2024)
- [Aspirations and Practice of Model Documentation: Moving the Needle with Nudging and Traceability](https://mlsanity.com/paper/2204.06425/) (2022)
- [Does More Advice Help? The Effects of Second Opinions in AI-Assisted Decision Making](https://mlsanity.com/paper/2401.07058/) (2024)
- [AmadeusGPT: a natural language interface for interactive animal behavioral analysis](https://mlsanity.com/paper/2307.04858/) (2023)
- [Competent but Rigid: Identifying the Gap in Empowering AI to Participate Equally in Group Decision-Making](https://mlsanity.com/paper/2302.08807/) (2023)
- [DatawiseAgent: A Notebook-Centric LLM Agent Framework for Adaptive and Robust Data Science Automation](https://mlsanity.com/paper/2503.07044/) (2025)
- [Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in Education](https://mlsanity.com/paper/2311.05792/) (2023)
- [Human-AI Experience in Integrated Development Environments: A Systematic Literature Review](https://mlsanity.com/paper/2503.06195/) (2025)
- [Mind the Metrics: Patterns for Telemetry-Aware In-IDE AI Application Development using the Model Context Protocol (MCP)](https://mlsanity.com/paper/2506.11019/) (2025)
- [Cognitive Spillover in Human-AI Teams](https://mlsanity.com/paper/2407.17489/) (2024)
- [Contextualized Data-Wrangling Code Generation in Computational Notebooks](https://mlsanity.com/paper/2409.13551/) (2024)
- [A moving target in AI-assisted decision-making: Dataset shift, model updating, and the problem of update opacity](https://mlsanity.com/paper/2504.05210/) (2025)
- [ReSpark: Leveraging Previous Data Reports as References to Generate New Reports with LLMs](https://mlsanity.com/paper/2502.02329/) (2025)
- [Using Small Language Models to Reverse-Engineer Machine Learning Pipelines Structures](https://mlsanity.com/paper/2601.03988/) (2026)
- [Automated Modernization of Machine Learning Engineering Notebooks for Reproducibility](https://mlsanity.com/paper/2602.07195/) (2026)
- [Predicting the Understandability of Computational Notebooks through Code Metrics Analysis](https://mlsanity.com/paper/2406.10989/) (2024)

## Abstract (arXiv)

Computational notebooks allow data scientists to express their ideas through a combination of code and documentation. However, data scientists often pay attention only to the code, and neglect creating or updating their documentation during quick iterations. Inspired by human documentation practices learned from 80 highly-voted Kaggle notebooks, we design and implement Themisto, an automated documentation generation system to explore how human-centered AI systems can support human data scientists in the machine learning code documentation scenario. Themisto facilitates the creation of documentation via three approaches: a deep-learning-based approach to generate documentation for source code, a query-based approach to retrieve online API documentation for source code, and a user prompt approach to nudge users to write documentation. We evaluated Themisto in a within-subjects experiment with 24 data science practitioners, and found that automated documentation generation techniques reduced the time for writing documentation, reminded participants to document code they would have ignored, and improved participants' satisfaction with their computational notebook.