# A large-scale comparative analysis of Coding Standard conformance in Open-Source Data Science projects

arXiv: https://arxiv.org/abs/2007.08978 · HTML: https://mlsanity.com/paper/2007.08978/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2020.

## Built on by (most cited first, 9 of 9)

- [The Prevalence of Code Smells in Machine Learning projects](https://mlsanity.com/paper/2103.04146/) (2021)
- [Code Smells for Machine Learning Applications](https://mlsanity.com/paper/2203.13746/) (2022)
- [A Large-Scale Study of Model Integration in ML-Enabled Software Systems](https://mlsanity.com/paper/2408.06226/) (2024)
- [Machine Learning Model Development from a Software Engineering Perspective: A Systematic Literature Review](https://mlsanity.com/paper/2102.07574/) (2021)
- [MLSmellHound: A Context-Aware Code Analysis Tool](https://mlsanity.com/paper/2205.03790/) (2022)
- [Comparative analysis of real bugs in open-source Machine Learning projects -- A Registered Report](https://mlsanity.com/paper/2209.09932/) (2022)
- [Automatic Identification of Machine Learning-Specific Code Smells](https://mlsanity.com/paper/2508.02541/) (2025)
- [Analysing Python Machine Learning Notebooks with Moose](https://mlsanity.com/paper/2509.11748/) (2025)
- [Still Manual? Automated Linter Configuration via DSL-Based LLM Compilation of Coding Standards](https://mlsanity.com/paper/2602.07783/) (2026)

## Abstract (arXiv)

Background: Meeting the growing industry demand for Data Science requires cross-disciplinary teams that can translate machine learning research into production-ready code. Software engineering teams value adherence to coding standards as an indication of code readability, maintainability, and developer expertise. However, there are no large-scale empirical studies of coding standards focused specifically on Data Science projects. Aims: This study investigates the extent to which Data Science projects follow code standards. In particular, which standards are followed, which are ignored, and how does this differ to traditional software projects? Method: We compare a corpus of 1048 Open-Source Data Science projects to a reference group of 1099 non-Data Science projects with a similar level of quality and maturity. Results: Data Science projects suffer from a significantly higher rate of functions that use an excessive numbers of parameters and local variables. Data Science projects also follow different variable naming conventions to non-Data Science projects. Conclusions: The differences indicate that Data Science codebases are distinct from traditional software codebases and do not follow traditional software engineering conventions. Our conjecture is that this may be because traditional software engineering conventions are inappropriate in the context of Data Science projects.