Detecting and Explaining Conflicts in Attributed Feature Models

TL;DR

This paper presents an approach to efficiently detect and explain contradictions in attributed feature models, aiding developers in correcting mistakes in product configuration systems.

Contribution

It introduces a method that translates attributed feature models into constraint satisfaction problems and uses QuickXplain to identify conflicting relations.

Findings

Effective detection of contradictions in attributed feature models.

Automated explanation of conflicts to assist developers.

Improved accuracy in maintaining consistent variability models.

Abstract

Product configuration systems are often based on a variability model. The development of a variability model is a time consuming and error-prone process. Considering the ongoing development of products, the variability model has to be adapted frequently. These changes often lead to mistakes, such that some products cannot be derived from the model anymore, that undesired products are derivable or that there are contradictions in the variability model. In this paper, we propose an approach to discover and to explain contradictions in attributed feature models efficiently in order to assist the developer with the correction of mistakes. We use extended feature models with attributes and arithmetic constraints, translate them into a constraint satisfaction problem and explore those for contradictions. When a contradiction is found, the constraints are searched for a set of contradicting relations by the QuickXplain algorithm.