What is it about?
Machine learning projects often involve complex code that can be hard to understand and maintain. Typically, this code is written by data scientists who may not always follow best practices in software design. In our study, we explored whether using SOLID design principles—guidelines that help make code more organized and easier to manage—could improve the understandability of machine learning code. We conducted experiments with 100 data scientists, showing some machine learning code written in the usual, unstructured way and others in the same code reorganized using SOLID principles. We found that those who reviewed the SOLID-structured code had a better understanding of it. This suggests that applying these design principles can make machine learning code easier to work with, potentially making the work of data scientists more efficient and effective. We recommend adopting these software design principles more widely in the data science community.
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Why is it important?
Our work is important because it addresses a significant gap in the intersection of software engineering and data science. Machine learning projects are often developed without strict adherence to software design principles, leading to code that can be difficult to understand and maintain. This is especially timely as the field of machine learning is rapidly expanding, with more data scientists from diverse backgrounds entering the field. What makes our work unique is the empirical evidence we provide through controlled experiments with 100 data scientists. By demonstrating that SOLID design principles can significantly enhance code understanding, we offer the possibility of a solution to a pervasive problem. This has the potential to make machine learning projects more sustainable and collaborative, benefiting individual data scientists and the broader tech community.
Perspectives
Writing this article was a particularly rewarding experience for me. As someone who has worked at the intersection of software engineering and data science, I have often seen the challenges that arise from poorly structured machine learning code. By investigating the impact of SOLID design principles, I hoped to bridge a gap between two fields I am passionate about. This research has the potential to significantly enhance the way data scientists approach coding, making their projects more sustainable and collaborative. Personally, I am excited about the possibility of fostering a stronger connection between software engineering best practices and the rapidly evolving field of machine learning. I hope this work will inspire others in the data science community to adopt these principles, ultimately leading to more efficient and maintainable codebases.
Raphael Cabral
Pontificia Universidade Catolica do Rio de Janeiro
Read the Original
This page is a summary of: Investigating the Impact of SOLID Design Principles on Machine Learning Code Understanding, April 2024, ACM (Association for Computing Machinery),
DOI: 10.1145/3644815.3644957.
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