Causality-based Explanations for Feature Model Configuration

Feature model (FM) configuration can be supported on the basis of different reasoning approaches such as SAT solving, constraint solving, and answer set programming (ASP).To better understand the reasons of including or excluding specific features, feature model configurations (or parts thereof) need to be explained to the user.In this paper, we introduce an algorithmic approach to determine minimal causality-based explanations which refer to those customer requirements and constraints directly responsible for a feature model configuration.This approach helps to create more transparency and understandability of feature model configuration by determining those attributes and constraints directly responsible for a specific configuration result.