Web services are the basic building blocks for the business which is different from web applications. Testing of web services is difficult and increases the cost due to the unavailability of source code. Researchers have, web services are tested based on the syntactic structure using Web Service Description Language (WSDL) for atomic web services. This paper proposes an automated testing framework for composite web services based on semantics where the domain knowledge of the web services is described using
protégé tool [4] and the behaviour of the entire business operation flow for the composite web service is described by Ontology Web Language for services (OWL-S)[1]. Prioritization of test cases is performed based on various coverage criteria for composite web services.Series of experiments were conducted to assess the effectiveness of prioritization and empirical results shown that prioritization techniques perform well in detecting faults compared to traditional techniques.
Nowadays, remote sensing technology is being used as an essential tool for monitoring and detecting oil spills to take precautions and to prevent the damages to the marine environment. As an important branch of remote sensing, satellite based synthetic aperture radar imagery (SAR) is the most effective way to accomplish these tasks. Since a marine surface with oil spill seems as a dark object because of much lower backscattered energy, the main problem is to recognize and differentiate the dark objects of oil spills from others to be formed by oceanographic and atmospheric conditions. In this study, Radarsat-1 images covering Lebanese coasts were employed for oil spill detection. For this purpose, a powerful classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) was used. As the original contribution of the paper, the network was trained by a novel heuristic optimization algorithm known as Artificial Bee Colony (ABC) method besides the conventional Backpropagation (BP) and Levenberg-Marquardt (LM) learning algorithms. A comparison and evaluation of different network training algorithms regarding reliability of detection and robustness show that for this problem best result is achieved with the Artificial Bee Colony algorithm (ABC).
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