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This paper proposes a methodological framework for systematically integrating artificial intelligence into Product Line Engineering (PLE) processes, aiming to optimize feature selection and variability management in complex system development. The study evaluates this approach through multi-case industrial scenarios to demonstrate how AI can enhance the efficiency of reusing shared assets within product families. While PLE is an established software engineering discipline, the core innovation here lies specifically in applying machine learning techniques to automate critical decision-making steps traditionally handled manually or via rule-based systems.
AI4PLE: A Methodology for Integrating AI into Product Line Engineering
arXiv:2607.22260v1 Announce Type: new 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.
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.
Artificial Intelligence (cs.AI)
arXiv:2607.22260 [cs.AI]
(or arXiv:2607.22260v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.22260 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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