SELM: Software Engineering of Machine Learning Models

TL;DR

The paper introduces the SELM framework for applying software engineering principles to improve the development, efficiency, and accuracy of machine learning models, emphasizing interdisciplinary collaboration.

Contribution

It presents a novel framework that integrates software engineering into machine learning model development, enhancing efficiency and resource utilization.

Findings

Improved learning accuracy with less hardware resources

Reduced training dataset size needed for effective models

Enhanced process efficiency through the SELM framework

Abstract

One of the pillars of any machine learning model is its concepts. Using software engineering, we can engineer these concepts and then develop and expand them. In this article, we present a SELM framework for Software Engineering of machine Learning Models. We then evaluate this framework through a case study. Using the SELM framework, we can improve a machine learning process efficiency and provide more accuracy in learning with less processing hardware resources and a smaller training dataset. This issue highlights the importance of an interdisciplinary approach to machine learning. Therefore, in this article, we have provided interdisciplinary teams' proposals for machine learning.