Enhancing Secure Development in Globally Distributed Software Product Lines: A Machine Learning-Powered Framework for Cyber-Resilient Ecosystems

Abstract

Embracing software product lines (SPLs) is pivotal in the dynamic landscape of contemporary software development. However, the flexibility and global distribution inherent in modern systems pose significant challenges to managing SPL variability, underscoring the critical importance of robust cybersecurity measures. This paper advocates for leveraging machine learning (ML) to address variability management issues and fortify the security of SPL. In the context of the broader special issue theme on innovative cybersecurity approaches, our proposed ML-based framework offers an interdisciplinary perspective, blending insights from computing, social sciences, and business. Specifically, it employs ML for demand analysis, dynamic feature extraction, and enhanced feature selection in distributed settings, contributing to cyber-resilient ecosystems. Our experiments demonstrate the framework's superiority, emphasizing its potential to boost productivity and security in SPLs. As digital threats evolve, this research catalyzes interdisciplinary collaborations, aligning with the special issue's goal of breaking down academic barriers to strengthen digital ecosystems against sophisticated attacks while upholding ethics, privacy, and human values.