# Assessing the Quality of Computational Notebooks for a Frictionless Transition from Exploration to Production

arXiv: https://arxiv.org/abs/2205.11941 · HTML: https://mlsanity.com/paper/2205.11941/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 24 May 2022.

## Builds on (most cited first, 14 of 14)

- [How do Data Science Workers Collaborate? Roles, Workflows, and Tools](https://mlsanity.com/paper/2001.06684/) (2020)
- [Guidelines for including grey literature and conducting multivocal literature reviews in software engineering](https://mlsanity.com/paper/1707.02553/) (2017)
- [Software Engineering Challenges of Deep Learning](https://mlsanity.com/paper/1810.12034/) (2018)
- [Adoption and Effects of Software Engineering Best Practices in Machine Learning](https://mlsanity.com/paper/2007.14130/) (2020)
- [Studying Software Engineering Patterns for Designing Machine Learning Systems](https://mlsanity.com/paper/1910.04736/) (2019)
- [Characterizing Technical Debt and Antipatterns in AI-Based Systems: A Systematic Mapping Study](https://mlsanity.com/paper/2103.09783/) (2021)
- [Characterizing and Detecting Mismatch in Machine-Learning-Enabled Systems](https://mlsanity.com/paper/2103.14101/) (2021)
- [The Prevalence of Code Smells in Machine Learning projects](https://mlsanity.com/paper/2103.04146/) (2021)
- [Better Code, Better Sharing:On the Need of Analyzing Jupyter Notebooks](https://mlsanity.com/paper/1906.05234/) (2019)
- [KGTorrent: A Dataset of Python Jupyter Notebooks from Kaggle](https://mlsanity.com/paper/2103.10558/) (2021)
- [Ten Simple Rules for Reproducible Research in Jupyter Notebooks](https://mlsanity.com/paper/1810.08055/) (2018)
- [Code Duplication and Reuse in Jupyter Notebooks](https://mlsanity.com/paper/2005.13709/) (2020)
- [Component Mismatches Are a Critical Bottleneck to Fielding AI-Enabled Systems in the Public Sector](https://mlsanity.com/paper/1910.06136/) (2019)
- [Towards Productizing AI/ML Models: An Industry Perspective from Data Scientists](https://mlsanity.com/paper/2103.10548/) (2021)

## Built on by (most cited first, 1 of 1)

- [Predicting the Understandability of Computational Notebooks through Code Metrics Analysis](https://mlsanity.com/paper/2406.10989/) (2024)

## Abstract (arXiv)

The massive trend of integrating data-driven AI capabilities into traditional software systems is rising new intriguing challenges. One of such challenges is achieving a smooth transition from the explorative phase of Machine Learning projects - in which data scientists build prototypical models in the lab - to their production phase - in which software engineers translate prototypes into production-ready AI components. To narrow down the gap between these two phases, tools and practices adopted by data scientists might be improved by incorporating consolidated software engineering solutions. In particular, computational notebooks have a prominent role in determining the quality of data science prototypes. In my research project, I address this challenge by studying the best practices for collaboration with computational notebooks and proposing proof-of-concept tools to foster guidelines compliance.