Informed decision-making in prioritising product variants

Feature models (FMs) play a crucial role in software product lines (SPLs) by representing variability and enabling the generation of diverse product configurations. However, the vast number of possible configurations often makes it challenging to identify the most suitable variant, especially when multiple criteria must be considered. Multi-criteria decision-making (MCDM) methods, such as analytic hierarchy process (AHP), technique for order of preference by similarity to ideal solution (TOPSIS), and VIseKriterijumska Optimizacija I Kompromisno Resenje ("multicriteria optimization and compromise solution") (VIKOR), are effective for ranking configurations based on user-defined preferences. However, the application of disparate MCDM techniques to the same feature model with identical criteria can yield conflicting rankings, thereby complicating the decision-making process. To address this issue, we propose a novel framework that systematically integrates multiple MCDM methods to prioritise product configurations and provides informed decision support to reconcile ranking discrepancies. The framework automates the prioritisation process and offers a structured approach to explain differences between rankings, enhancing transparency and user confidence in the final selection. The framework's effectiveness has been validated through real-world case studies, demonstrating its ability to streamline configuration prioritisation and support consistent, preference-driven decision-making in complex SPL environments.