Confidence

Feature models have been widely adopted to reuse the re-quirements of a set of similar products in a domain. When constructing feature models, it is difficult to always ensure the consistency of feature models. Therefore, tolerating in-consistencies is important during the construction of feature models. The usual way of tolerating inconsistencies is to find the minimal unsatisfiable core. However, identifying the minimal unsatisfiable core is time-consuming, which de-creases itself the practicability. In this paper, we propose a priority based approach to tolerating inconsistencies in feature models efficiently. The basic idea of our approach is to find the weaker unsatis-fied constraints, while keeping the rest of the feature model consistent. Our approach tolerates inconsistencies with the help of priority based operations while building feature mod-els. To this end, we adopt the constraint hierarchy the-ory to express the degree of domain analysts ’ confidence on constraints (i.e. the priorities of constraints) and tolerate inconsistencies in feature models. Experiments have been conducted to demonstrate that our system can scale up to large feature models