Variability Exploration for Decision Making: Supporting Domain Experts in Configuring Business Processes.
Year: 2025
Type: article
Abstract: Designing a model with built-in variability that can later be specialized for specific needs has become a common practice.This approach enables the consolidation of company expertise within a single model, extending its applicability beyond a single system, process, or behavior.Such modeling reveals a set of choices that Domain Experts (DEs) must evaluate, collectively forming the variability space-the range of potential decisions available to achieve desired project outcomes.Selecting the optimal decision within this variability space is often challenging for DEs, especially those without a technical background.The abundance of alternatives, each with the potential to significantly influence the capabilities of the final solution, adds complexity to the decision-making process.To assist DEs in exploring their models, we propose a tool-supported method for discovering and visualizing the variability space captured within feature models.This method allows experts to explore and evaluate different options against predefined objectives.By representing the variability space in a format conducive to decision-making, our method helps identify key choices that impact overall business processes, assess the implications of each option, and explore alternative configurations.We validate this method through a simplified case study of the OneWay project of Airbus, a leading international aircraft manufacturer.Applying our method to their feature model and business processes for avionics program development planning demonstrated its effectiveness in supporting decision-making activities and its overall performance.
Source: The Journal of Object Technology
Institutions: Mohammed V University, Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Institut de Recherche en Informatique et Systèmes Aléatoires, Université de Rennes, DIVERSE: Diversity-centric Software Engineering, King's College London
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411187758