Feature models have been widely adopted to reuse the requirements 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 inconsistencies 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 decreases 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 unsatisfied constraints, keeping the rest of the feature model consistent. Our approach tolerates inconsistencies with the help of priority based operations while building feature models. To this end, we adopt the constraint hierarchy theory 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 the scalability of our approach.
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