Abstract. In the field of requirements engineering, measuring inconsistency is crucial to effective inconsistency management. A practical measure must consider both the degree and significance of inconsistency in specification. The main contribution of this paper is providing an approach for measuring inconsistent specification in terms of the prioritybased scoring vector, which integrates the measure of the degree of inconsistency with the measure of the significance of inconsistency. In detail, for each specification ∆ that consists of a set of requirements statements, if L is a m-level priority set, we define a m-dimensional priority-based significance vector − → V to measure the significance of the inconsistency in ∆. Furthermore, a priority-based scoring vector − → SP : P(∆) → N m+1 has been defined to provide an ordering relation over specifications that describes which specification is "more essentially inconsistent than" others.

It is increasingly recognized that identifying the degree of blame or responsibility of each formula for inconsistency of a knowledge base (i.e. a set of formulas) is useful for making rational decisions to resolve inconsistency in that knowledge base. Most current techniques for measuring the blame of each formula with regard to an inconsistent knowledge base focus on classical knowledge bases only. Proposals for measuring the blames of formulas with regard to an inconsistent prioritized knowledge base have not yet been given much consideration. However, the notion of priority is important in inconsistencytolerant reasoning. This article investigates this issue and presents a family of measurements for the degree of blame of each formula in an inconsistent prioritized knowledge base by using the minimal inconsistent subsets of that knowledge base. First of all, we present a set of intuitive postulates as general criteria to characterize rational measurements for the blames of formulas of an inconsistent prioritized knowledge base. Then we present a family of measurements for the blame of each formula in an inconsistent prioritized knowledge base under the guidance of the principle of proportionality, one of the intuitive postulates. We also demonstrate that each of these measurements possesses the properties that it ought to have. Finally, we use a simple but explanatory example in requirements engineering to illustrate the application of these measurements. Compared to the related works, the postulates presented in this article consider the special characteristics of minimal inconsistent subsets as well as the priority levels of formulas. This makes them more appropriate to characterizing the inconsistency measures defined from minimal inconsistent subsets for prioritized knowledge bases as well as classical knowledge bases. Correspondingly, the measures guided by these postulates can intuitively capture the inconsistency for prioritized knowledge bases.

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