Requirement engineering is one of the critical phases in the software development process. Functional Requirements (FR) and Non-Functional Requirements(NFR) are two of the fundamental requirements in software projects that are observed in the classifications of most researchers in the software engineering field. Conflicting and overlapping among the requirements in both intra and extra communications levels are one of the main challenges in the elicitation and prioritization phases. This paper presents a decision strategy to respond to this challenge called requirements conflicts management strategy (RCMS). This strategy is defined to manage conflict and overlap of NFRs in the prioritization of the constraints satisfaction model for requirements prioritization, known as "CSOP + RP" model, to which the necessary constraints are applied. RCMS is applied to the "CSOP+RP" model as a pre-processing phase by the requirement analyzer and the results are delivered to the system manager. RCMS is founded on several components: the conflicts catalog among NFRs, the mapping model of NFRs to the domain of software systems, and the calculation of Pearson correlation coefficients in NFRs. The negative, positive, and zero values of the correlation coefficients are calculated on the importance of the requirements, which mean conflict, overlap and neutral, respectively. RCMS was implemented on Police Command-and-Control System(PCCS) as a designed case study with specific NFRs and FRs. Therefore, the statistical analysis of the experimental results shows that the proposed strategy increases the accuracy of the input values of the prioritization model and better decision-making in managing conflicts and controlling overlaps. Furthermore, RCMS help to reduce the ambiguities between NFRs and FRs and also influences of NFRs in requirement ranking by the search-based prioritization approach. تفاصيل المقالة

With the growth of technology, supervising systems are increasingly replacing humans in military, transportation, medical, spatial, and other industries. Among these systems are machine vision systems which are based on image processing and analysis. One of the important tasks of image processing is classification of images into desirable categories for the identification of objects or their specific areas. One of the common methods is using an edge finder in image classification. Due to the lack of definite edges in many images obtained from various sciences and industries such as textural images, the topic of textural image classification has recently become of interest in the science of machine vision. Thus, in this article, two methods are proposed to detect edges and eliminate blocks with non-connected classes based on fuzzy theory and weighted voting concepts in classifying textural images. In the proposed methods, the boundaries are corrected using fuzzy theory and weighted voting concepts. Using the proposed methods can help improve the definition of boundaries and classification accuracy. تفاصيل المقالة