Variability Exploration for Decision Making: Supporting Domain Experts in Configuring Business Processes

By: Haitam El Hayani, Benoit Combemale, Olivier Barais, Steffen Zschaler

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.

Keywords

Variability management, Software product lines, Evolutionary algorithms.

Cite as:

Haitam El Hayani, Benoit Combemale, Olivier Barais, Steffen Zschaler, “Variability Exploration for Decision Making: Supporting Domain Experts in Configuring Business Processes”, Journal of Object Technology, Volume 24, no. 2 (May 2025), pp. 2:1-13, doi:10.5381/jot.2025.24.2.a3.