Work

Intelligence as a Feature: Modeling Ml in Software Product Lines

Year: 2025

Type: article

Abstract: 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.

Source: International Journal of Software Engineering & Applications

DOI: 10.5121/ijsea.2025.16601

Authors: Luz-Viviana Cobaleda, Andrés López, Paola Vallejo, Raúl Mazo, Julián Carvajal

Institutions: Universidad de Antioquia, Laboratoire des Sciences et Techniques de l’Information de la Communication et de la Connaissance, École nationale supérieure de techniques avancées Bretagne, Universidad EAFIT

Language: English

Topic: Advanced Software Engineering Methodologies

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: bronze

Funder: Universidad de Antioquia