Work

Causality-based Explanations for Feature Model Configuration

Year: 2025

Type: conference-paper

Abstract: 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.

Source: International Working Conference on Variability Modelling of Software-Intensive Systems (VaMoS)

DOI: 10.1145/3715340.3715438

Authors: Alexander Felfernig, Damian Garber, Viet-Man Le, Sebastian Lubos

Institution: Graz University of Technology

Language: English

Topic: Advanced Software Engineering Methodologies

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Österreichische Forschungsförderungsgesellschaft

Award: G7963070552