# Machine Learning Model Development from a Software Engineering Perspective: A Systematic Literature Review

arXiv: https://arxiv.org/abs/2102.07574 · HTML: https://mlsanity.com/paper/2102.07574/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 15 Feb 2021.

## Builds on (most cited first, 11 of 11)

- [Towards Accountability for Machine Learning Datasets: Practices from Software Engineering and Infrastructure](https://mlsanity.com/paper/2010.13561/) (2020)
- [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)
- [Importance-Driven Deep Learning System Testing](https://mlsanity.com/paper/2002.03433/) (2020)
- [Software engineering for artificial intelligence and machine learning software: A systematic literature review](https://mlsanity.com/paper/2011.03751/) (2020)
- [Machine Learning Software Engineering in Practice: An Industrial Case Study](https://mlsanity.com/paper/1906.07154/) (2019)
- [A large-scale comparative analysis of Coding Standard conformance in Open-Source Data Science projects](https://mlsanity.com/paper/2007.08978/) (2020)
- [Characterizing machine learning process: A maturity framework](https://mlsanity.com/paper/1811.04871/) (2018)
- [Interpreting Cloud Computer Vision Pain-Points: A Mining Study of Stack Overflow](https://mlsanity.com/paper/2001.10130/) (2020)
- [Synergy between Machine/Deep Learning and Software Engineering: How Far Are We?](https://mlsanity.com/paper/2008.05515/) (2020)
- [Analysis of Software Engineering for Agile Machine Learning Projects](https://mlsanity.com/paper/1912.07323/) (2019)

## Built on by (most cited first, 4 of 4)

- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practice](https://mlsanity.com/paper/2301.09001/) (2023)
- [Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Vision](https://mlsanity.com/paper/2303.07476/) (2023)
- [How Mature is Requirements Engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directions](https://mlsanity.com/paper/2409.07192/) (2024)

## Abstract (arXiv)

Data scientists often develop machine learning models to solve a variety of problems in the industry and academy but not without facing several challenges in terms of Model Development. The problems regarding Machine Learning Development involves the fact that such professionals do not realize that they usually perform ad-hoc practices that could be improved by the adoption of activities presented in the Software Engineering Development Lifecycle. Of course, since machine learning systems are different from traditional Software systems, some differences in their respective development processes are to be expected. In this context, this paper is an effort to investigate the challenges and practices that emerge during the development of ML models from the software engineering perspective by focusing on understanding how software developers could benefit from applying or adapting the traditional software engineering process to the Machine Learning workflow.