In the age of Information and Communication Technology (ICT), Web and the Internet have changed significantly the way applications are developed, deployed and used. One of recent trends is modern design of web-applications based on SOA. This process is based on the composition of existing web services into a single scenario from the point of view of a particular user or client. This allows IT companies to shorten the product-time to market process. On the other hand, it raises questions about the quality of the application, trade-offs between quality factors and attributes and measurements of these. Services are usually hosted and executed in an environment managed by its provider that assures the quality attributes such as availability or throughput. Therefore, in this paper an attempt has been made to perform quality measurements towards the creation of efficient, dependable and user-oriented Web applications. First, the process of designing service-based applications is described. Next, metrics for subsequent measurements of efficiency, dependability and usability of distributed applications are presented. These metrics will assess the efforts and trade-offs in a Web-based application development. As examples, we describe a pair of multimedia applications which we have developed in our department and executed in a cluster-based environment. One of them runs in the BeesyCluster middleware and the second one in the Kaskada platform. For these applications we present results of measurements and conclude about relations between quality attributes in the presented application development model. This knowledge can be used to reason about such relations for new similar applications and be used in rapid and quality development of the latter.

In this paper, a modified sound quality evaluation (SQE) model is developed based on combination of an optimized artificial neural network (ANN) and the wavelet packet transform (WPT). The presented SQE model is a signal processing technique, which can be implemented in current microphones for predicting the sound quality. The proposed method extracts objective psychoacoustic metrics including loudness, sharpness, roughness, and tonality from sound samples, by using a special selection of multi-level nodes of the WPT combined with a trained ANN. The model is optimized using the particle swarm optimization (PSO) and the back propagation (BP) algorithms. The obtained results reveal that the proposed model shows the lowest mean square error and the highest correlation with human perception while it has the lowest computational cost compared to those of the other models and software.