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International Journal of Information, Control and Computer Sciences

ISSN: 2517-9942

Total 42 content found

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A Design and Implementation Model for Web Caching Using Server “URL Rewriting“

Year: 2009 Volume: 3 Issue: 12 2741 - 2746 Pages

Abstract: In order to make surfing the internet faster, and to save redundant processing load with each request for the same web page, many caching techniques have been developed to reduce latency of retrieving data on World Wide Web. In this paper we will give a quick overview of existing web caching techniques used for dynamic web pages then we will introduce a design and implementation model that take advantage of “URL Rewriting" feature in some popular web servers, e.g. Apache, to provide an effective approach of caching dynamic web pages.

A Growing Natural Gas Approach for Evaluating Quality of Software Modules

Year: 2009 Volume: 3 Issue: 12 2737 - 2740 Pages

Abstract: The prediction of Software quality during development life cycle of software project helps the development organization to make efficient use of available resource to produce the product of highest quality. “Whether a module is faulty or not" approach can be used to predict quality of a software module. There are numbers of software quality prediction models described in the literature based upon genetic algorithms, artificial neural network and other data mining algorithms. One of the promising aspects for quality prediction is based on clustering techniques. Most quality prediction models that are based on clustering techniques make use of K-means, Mixture-of-Guassians, Self-Organizing Map, Neural Gas and fuzzy K-means algorithm for prediction. In all these techniques a predefined structure is required that is number of neurons or clusters should be known before we start clustering process. But in case of Growing Neural Gas there is no need of predetermining the quantity of neurons and the topology of the structure to be used and it starts with a minimal neurons structure that is incremented during training until it reaches a maximum number user defined limits for clusters. Hence, in this work we have used Growing Neural Gas as underlying cluster algorithm that produces the initial set of labeled cluster from training data set and thereafter this set of clusters is used to predict the quality of test data set of software modules. The best testing results shows 80% accuracy in evaluating the quality of software modules. Hence, the proposed technique can be used by programmers in evaluating the quality of modules during software development.