Luz-Viviana Cobaleda¹, Julian Carvajal¹, Paola Vallejo², Andres Lopez¹, and Raul Mazo³, ¹Universidad de Antioquia, Colombia, ²3ENSTA, Francia

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.

Machine Learning (ML), Software Product Lines (SPL), ML-based systems, variability modeling.