Investigating the Impact of SOLID Design Principles on Machine Learning Code Understanding

TL;DR

This study investigates how applying SOLID design principles to machine learning code affects understanding, finding that such principles significantly improve code comprehension among data scientists.

Contribution

It provides empirical evidence that SOLID principles enhance ML code understanding, advocating for their adoption in data science practices.

Findings

SOLID principles improve ML code understanding

Statistically significant positive effect observed

Supports spreading software engineering practices in ML community

Abstract

[Context] Applying design principles has long been acknowledged as beneficial for understanding and maintainability in traditional software projects. These benefits may similarly hold for Machine Learning (ML) projects, which involve iterative experimentation with data, models, and algorithms. However, ML components are often developed by data scientists with diverse educational backgrounds, potentially resulting in code that doesn't adhere to software design best practices. [Goal] In order to better understand this phenomenon, we investigated the impact of the SOLID design principles on ML code understanding. [Method] We conducted a controlled experiment with three independent trials involving 100 data scientists. We restructured real industrial ML code that did not use SOLID principles. Within each trial, one group was presented with the original ML code, while the other was presented with ML code incorporating SOLID principles. Participants of both groups were asked to analyze the code and fill out a questionnaire that included both open-ended and closed-ended questions on their understanding. [Results] The study results provide statistically significant evidence that the adoption of the SOLID design principles can improve code understanding within the realm of ML projects. [Conclusion] We put forward that software engineering design principles should be spread within the data science community and considered for enhancing the maintainability of ML code.