AI4PLE: A Methodology for Integrating AI into Product Line Engineering

2026-07-24

AI Summary

This work addresses the long-standing lack of systematic artificial intelligence support for critical decisions in product line engineering (PLE), such as feature selection, variability management, and configuration optimization. It proposes the first AI-integrated methodological framework specifically designed for PLE, which organically combines established product line engineering principles with advanced artificial intelligence techniques to enable intelligent decision-making across these core activities. Validated through multiple industrial case studies, the framework demonstrably enhances the automation and intelligence of product family development, offering a reusable and scalable pathway for AI-driven transformation in PLE.

Abstract

Reuse-based development has become increasingly important in the creation of complex systems, offering significant opportunities to reduce costs, improve quality, and accelerate time-to-market. Product Line Engineering (PLE) provides a systematic approach to realizing this potential by enabling the efficient creation, management, and customization of product families by reusing shared assets and capabilities. PLE involves addressing numerous complex decisions, including feature selection, variability management, and configuration optimization, which are critical to the success of a product line. Despite its promise, the systematic integration of Artificial Intelligence (AI) into PLE processes has not yet been comprehensively explored. In this paper, we propose a methodological framework to support the systematic integration of AI into PLE and evaluate its effectiveness through a multi-case study conducted in an industrial context.

Problem

• Artificial Intelligence
• Product Line Engineering
• AI Integration
• Variability Management
• Configuration Optimization

Innovation

• AI4PLE
• Product Line Engineering
• Artificial Intelligence integration
• variability management
• systematic methodology