Mining binary constraints in the construction of feature models

Feature models provide an effective way to organize and reuse requirements in a specific domain. A feature model consists of a feature tree and cross-tree constraints. Identifying features and then building a feature tree takes a lot of effort, and many semi-automated approaches have been proposed to help the situation. However, finding cross-tree constraints is often more challenging which still lacks the help of automation. In this paper, we propose an approach to mining cross-tree binary constraints in the construction of feature models. Binary constraints are the most basic kind of cross-tree constraints that involve exactly two features and can be further classified into two sub-types, i.e. requires and excludes. Given these two sub-types, a pair of any two features in a feature model falls into one of the following classes: no constraints between them, a requires between them, or an excludes between them. Therefore we perform a 3-class classification on feature pairs to mine binary constraints from features. We incorporate a support vector machine as the classifier and utilize a genetic algorithm to optimize it. We conduct a series of experiments on two feature models constructed by third parties, to evaluate the effectiveness of our approach under different conditions that might occur in practical use. Results show that we can mine binary constraints at a high recall (near 100% in most cases), which is important because finding a missing constraint is very costly in real, often large, feature models.