INTELLIGENCE AS A FEATURE: MODELING ML IN SOFTWARE PRODUCT LINES

The integration of Machine Learning (ML) components into modern software systems enhances datadriven decision-making but introduces new challenges for Software Product Line (SPL) engineering. Variability modeling, configuration, and reuse become increasingly complex when adaptive ML components are involved. Although previous studies have addressed variability in traditional SPLs and ML integration in standalone systems, limited work has systematically explored the intersection of these two domains. This paper presents a structured framework that extends SPL engineering to support ML-aware variability management. The framework enables the systematic modeling and configuration of ML components and has been implemented in the VariaMos web tool. A case study demonstrates the framework’s feasibility and applicability, illustrating how it supports the development of adaptive and intelligent product lines.