Enhancing software product lines with machine learning components

TL;DR

This paper presents a structured framework to integrate machine learning components into software product lines, addressing variability management and reuse challenges, with a partial implementation via the VariaMos tool.

Contribution

It introduces a novel framework for modeling and managing variability in SPLs that include ML components, bridging a gap in existing approaches.

Findings

Framework supports systematic variability modeling

Partial implementation demonstrated with VariaMos tool

Enhances SPL engineering for ML integration

Abstract

Modern software systems increasingly integrate machine learning (ML) due to its advancements and ability to enhance data-driven decision-making. However, this integration introduces significant challenges for software engineering, especially in software product lines (SPLs), where managing variability and reuse becomes more complex with the inclusion of ML components. Although existing approaches have addressed variability management in SPLs and the integration of ML components in isolated systems, few have explored the intersection of both domains. Specifically, there is limited support for modeling and managing variability in SPLs that incorporate ML components. To bridge this gap, this article proposes a structured framework designed to extend Software Product Line engineering, facilitating the integration of ML components. It facilitates the design of SPLs with ML capabilities by enabling systematic modeling of variability and reuse. The proposal has been partially implemented with the VariaMos tool.