# Documentation of Machine Learning Software

arXiv: https://arxiv.org/abs/2001.11956 · HTML: https://mlsanity.com/paper/2001.11956/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2020.

## Built on by (most cited first, 8 of 8)

- [Retrieval-Augmented Test Generation: How Far Are We?](https://mlsanity.com/paper/2409.12682/) (2024)
- [What are the Machine Learning best practices reported by practitioners on Stack Exchange?](https://mlsanity.com/paper/2301.10516/) (2023)
- [Challenges in Migrating Imperative Deep Learning Programs to Graph Execution: An Empirical Study](https://mlsanity.com/paper/2201.09953/) (2022)
- [Do Not Take It for Granted: Comparing Open-Source Libraries for Software Development Effort Estimation](https://mlsanity.com/paper/2207.01705/) (2022)
- [A Systematic Literature Review on the Use of Machine Learning in Software Engineering](https://mlsanity.com/paper/2406.13877/) (2024)
- [Perspective of Software Engineering Researchers on Machine Learning Practices Regarding Research, Review, and Education](https://mlsanity.com/paper/2411.19304/) (2024)
- [Expanding ML-Documentation Standards For Better Security](https://mlsanity.com/paper/2507.12003/) (2025)
- [The Impact of Foundational Models on Patient-Centric e-Health Systems](https://mlsanity.com/paper/2507.21882/) (2025)

## Abstract (arXiv)

Machine Learning software documentation is different from most of the documentations that were studied in software engineering research. Often, the users of these documentations are not software experts. The increasing interest in using data science and in particular, machine learning in different fields attracted scientists and engineers with various levels of knowledge about programming and software engineering. Our ultimate goal is automated generation and adaptation of machine learning software documents for users with different levels of expertise. We are interested in understanding the nature and triggers of the problems and the impact of the users' levels of expertise in the process of documentation evolution. We will investigate the Stack Overflow Q/As and classify the documentation related Q/As within the machine learning domain to understand the types and triggers of the problems as well as the potential change requests to the documentation. We intend to use the results for building on top of the state of the art techniques for automatic documentation generation and extending on the adoption, summarization, and explanation of software functionalities.