Predicting Higher Order Structural Feature Interactions in Variable Systems

Abstract

Robust and effective support for the detection and management of software features and their interactions is crucial for many development tasks but has proven to be an elusive goal despite extensive research on the subject. This is especially challenging for variable systems where multiple variants of a system and their features must be collectively considered. Here an important issue is the typically large number of feature interactions that can occur in variable systems. We propose a method that computes, from a set of known source code level interactions of n features, the relevant interactions involving n+1 features. Our method is based on the insight that, if a set of features interact, it is much more likely that these features also interact with additional features, as opposed to completely different features interacting. This key insight enables us to drastically prune the space of potential feature interactions to those that will have a true impact at source code level. This substantial space reduction can be leveraged by analysis techniques that are based on feature interactions (e.g Combinatorial Interaction Testing). Our observation is based on eight variable systems, implemented in Java and C, totaling over nine million LoC, with over seven thousand feature interactions.