Towards Anomaly Explanation in Feature Models

Abstract

Feature models are a wide-spread approach to variability and commonality management in software product lines. Due to the increasing size and complexity of feature models, anomalies in terms of inconsistencies and redundancies can occur which lead to increased efforts related to feature model development and maintenance. In this paper we introduce knowledge representations which serve as a basis for the explanation of anomalies in feature models. On the basis of these representations we show how explanation algorithms can be applied. The results of a performance analysis show the applicability of these algorithms for anomaly detection in feature models. We conclude the paper with a discussion of future research issues.