# Safe-DS: A Domain Specific Language to Make Data Science Safe

arXiv: https://arxiv.org/abs/2302.14548 · HTML: https://mlsanity.com/paper/2302.14548/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 41 references that are in the map.
- First posted on arXiv 28 Feb 2023.

## Builds on (most cited first, 8 of 8)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [How do Data Science Workers Collaborate? Roles, Workflows, and Tools](https://mlsanity.com/paper/2001.06684/) (2020)
- [How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question?](https://mlsanity.com/paper/1909.03486/) (2019)
- [Ludwig: a type-based declarative deep learning toolbox](https://mlsanity.com/paper/1909.07930/) (2019)
- [Achieving Guidance in Applied Machine Learning through Software Engineering Techniques](https://mlsanity.com/paper/2203.15510/) (2022)
- [Improving the Learnability of Machine Learning APIs by Semi-Automated API Wrapping](https://mlsanity.com/paper/2203.15491/) (2022)

## Built on by (most cited first, 2 of 2)

- [3D Software Synthesis Driven by Constraint-Expressive Intermediate Representation](https://mlsanity.com/paper/2507.18625/) (2025)
- [Data-aware Static Analysis: Improving Detection of Semantic Faults in Machine Learning Code Using Data Characteristics](https://mlsanity.com/paper/2606.09957/) (2026)

## Abstract (arXiv)

Due to the long runtime of Data Science (DS) pipelines, even small programming mistakes can be very costly, if they are not detected statically. However, even basic static type checking of DS pipelines is difficult because most are written in Python. Static typing is available in Python only via external linters. These require static type annotations for parameters or results of functions, which many DS libraries do not provide. In this paper, we show how the wealth of Python DS libraries can be used in a statically safe way via Safe-DS, a domain specific language (DSL) for DS. Safe-DS catches conventional type errors plus errors related to range restrictions, data manipulation, and call order of functions, going well beyond the abilities of current Python linters. Python libraries are integrated into Safe-DS via a stub language for specifying the interface of its declarations, and an API-Editor that is able to extract type information from the code and documentation of Python libraries, and automatically generate suitable stubs. Moreover, Safe-DS complements textual DS pipelines with a graphical representation that eases safe development by preventing syntax errors. The seamless synchronization of textual and graphic view lets developers always choose the one best suited for their skills and current task. We think that Safe-DS can make DS development easier, faster, and more reliable, significantly reducing development costs.