Automated Diagnosis of Feature Model Configurations

Abstract

Feature models are widely used to model software product-line (SPL) variability. SPL variants are configured by selecting feature sets that satisfy feature model constraints. Configuration of large feature models involve multiple stages and participants, which makes it hard to avoid conflicts and errors. New techniques are therefore needed to debug invalid configurations and derive the minimal set of changes to fix flawed configurations. This paper provides three contributions to debugging feature model configurations: (1) we present a technique for transforming a flawed feature model configuration into a Constraint Satisfaction Problem (CSP) and show how a constraint solver can derive the minimal set of feature selection changes to fix an invalid configuration, (2) we show how this diagnosis CSP can automatically resolve con- flicts between configuration participant decisions, and (3) we present experiment results that evaluate our technique. These results show how our technique scales to models with over 5,000 features, which is beyond the size used to validate other automated techniques.