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In this paper, we introduce a formal approach for composing software components into a distributed system. We describe the system as a hierarchical composition of some components, which can be distributed on a wide variety of hardware platforms and executed in parallel. We represent each component by a mathematical model and specify the abstract communication protocols of the components using Interface Automata (IAs). To model hierarchical systems, besides the basic components' model, we will present other components, called nodes. A node consists of a set of subnodes interacting under the supervision of a controller. Each subnode, in turn, is a node or discrete event component. By considering a subnode as a node we can make hierarchical nodes/components. The entire system, therefore, forms the root of the hierarchy. A controller, in turn, is a set of subcontrollers/interface automata that specifies interaction protocol of the components inside a node. We have also presented an example demonstrating the model by illustrating nodes, subnodes, controllers, and subcontrollers. To address the state space explosion problem in system verification, we utilize the controller as a contract for independent analysis of the components and their interactions. Therefore, a node will not be analyzed directly; instead, we will analyze the controller.

Abstract-Search engine is one of the most important tools for managing the massive amount of d istributed web content. Web spamming tries to deceive search engines to rank some pages higher than they deserve. Many methods have been proposed to combat web spamming and to detect spam pages. One basic one is using classification, i.e., learn ing a classification model for classifying web pages to spam or non-spam. This work tries to select the best feature set for classification of web spam using imperialist competitive algorithm and genetic algorith m. Imperialist competitive algorith m is a novel optimization algorithm that is inspired by socio-political process of imperialis m in the real world. Experiments are carried out on W EBSPAM-UK2007 data set, which show feature selection improves classification accuracy, and imperialist competitive algorithm outperforms GA.