Information Technology

Testing is an accepted technique for improving the quality of developed software With the increase in size and complexity of modern software products, the importance of testing is rapidly growing. Regression testing plays a vital role for software maintenance when software is modified. The main purpose of regression testing is to ensure the bugs are fixed and the new functionality that are incorporated in a new version of a software do not unfavorably affect the correct functionality of the previous version. So to revalidate the modified software, regression testing is the right testing process. Though it is an expensive process which requires executing maintenance process frequently but it becomes necessary for subsequent version of test suites. To evaluate the quality of test cases which are used to test a program, testing requires execution of a program. In this paper we propose a new test case prioritization technique using genetic algorithm. The proposed technique separate the test case detected as severe by customer and among the rest test case prioritizes subsequences of the original test suite so that the new suite, which is run within a time-constrained execution environment, will have a superior rate of fault detection when compared to rates of randomly prioritized test suites. This experiment analyzes the genetic algorithm with regard to effectiveness and time overhead by utilizing structurally-based criterion to prioritize test cases.

In this paper, finite element model for flow of a viscous lubricant in a slider bearing is considered as a problem. For this problem, two different types of finite element models have been presented which are the velocity-pressure finite element model, with (u,v,P) as the primary nodal degrees of freedom and the penalty finite element model with (u,v) as the primary nodal degrees of freedom. Quadrilateral elements are used since they are more reliable for pressure as well as for velocity fields in the penalty finite element model.

Human resources are one of the vital assets for the success of every organization. In similar manner, in universities, each student’s performance in every schools determines the sustenance or depletion of the institution’s competitive edge towards its competitors. Hence this tracer studies was conducted to determine the present personal and professional profile as well as the IT skills and competencies possessed by the Information Technology (IT) graduates in the Philippines to evaluate the relevance and quality of the programs the universities offered and to prescribe the IT skills and competencies that are sought-after by the labor industries nowadays in the IT field.

Asynchronous Transfer Mode (ATM) is a connection-oriented packet switching technique that is universally accepted as the transfer mode of choice for Broadband Integrated Services Digital Network. The Quality of Service (QoS) capabilities of ATM is one of its major strength. In this paper we have explore some of the technological challenges and issues which plays important role in the security of ATM networks.

The research, development and the accuracy of automatic speech recognition (ASR) remains one of the most important research challenges over the years e.g. speaker and language variability, vocabulary size and domain, noise. This paper describes the recent progress and the author’s perspective of ASR and gives an overview of major technological perspective and appreciation of the fundamental progress of Automatic speech recognition.

New advancement in the field of Software Engineering increases more issues and problems. After reviewing quite a few research papers & articles in the field of Software Engineering, we obtained various challenges that rise in building a complex software system. This paper describes several challenges evolved in building a complex software system.

It is now evidently clear that with the emergence of social network and advanced technological messaging functionalities, students in Njala University Department of Physics and Computer Science are more likely to rely on WhatsApp messenger application for their day-to-day communications than any other social networking sites (SNSs). Because of some of the advantages WhatsApp messenger has like cost efficiency, effective, quick and easier mode of communication, confidential, and convenient usage are some of the major factors which might influence their decision to use it. Thus, the objective of the study was to investigate the impact of WhatsApp messenger on the academic performance of students in tertiary institutions (A study of Njala University Department of Physics and Computer Science, School of Technology) in 2014/2015 academic year. Both primary data and secondary data were used. A structured questionnaire was employed to collect data from a sample of 50 students in five different programs within the School of Technology in which 40% were male and 60% were female. The study revealed that majority of respondents used WhatsApp messenger for making friends and chatting. In addition, a majority of respondents experienced negative effects such as poor grammar and spelling, less study time and poor academic performance due to the heavy participation on social media networks. Furthermore, there was a high addiction rate among students in the usage of social media networks especially WhatsApp messenger. Nevertheless, there were cases where others experienced improvement in their readings skills as a result of participation on social media networks. Also, respondents shared ideas, discussed and shared examination questions among themselves on WhatsApp messenger. The study recommended the stringent enforcement of the administration of Njala University rule on electronic device usage in schools, promotion of social media usage for academic purpose, counseling for addicted students and the use of the right grammar and spelling when participating on social networks.

The purpose of this research is to classify sentiment of Twitter messages. We present a novel approach for automatically classifying the sentiment of Twitter messages. These messages are classified as either positive or negative. This is useful for consumers who want to do research on the user sentiment for products before purchase, or companies that want to monitor the public sentiment of their brands. Known supervised learning algorithms as support vector machines and Maximum Entropy are used to create a prediction model. Before the prediction model created, the data has pre-processed by using some properties of data such as username, hyperlink, and multiple occurrence of character in order to reduce feature space and achieve high accuracy.

Cloud Computing (CC) is emerging as the next generation platform which would facilitate the user on pay as you use mode as per requirement. It provides a number of benefits which could not otherwise be realized. The primary aim of CC is to provide efficient access to remote and geographically distributed resources. A scheduling algorithm is needed to manage the access to the different resources. There are different types of resource scheduling technologies in CC environment. These are implemented at different levels based on different parameters like cost, performance, resource utilization, time, priority, physical distances, throughput, bandwidth, resource availability etc. In this research paper various types of resource allocation scheduling algorithms that provide efficient cloud services have been surveyed and analyzed. Based on the study of different algorithms, a classification of the scheduling algorithms on the basis of selected features has been presented.

Utility mining is to discover the itemsets in a transaction database with utility values over a given threshold. Utility of itemsets are based upon user’s perception such as cost, profit or revenue and are of significant importance. Utility-based data mining intends discovering the itemsets with high total utility is called High Utility Itemset mining. High Utility itemsets may contain frequent as well as rare itemsets. Classical utility mining considers items alone as discrete values. In real world applications, such utilities can be described by fuzzy sets. In this paper, proposed an algorithm, FHUI (Fuzzy High Utility Itemset) Mining is presented to mine high utility itemsets effectively from large databases, by fuzzification of utility values. FHUI extracts fuzzy high utility itemsets by integrating fuzzy logic with high utility itemset mining. This algorithm finds all utility-frequent itemsets within given threshold value. The experimental result shows that, it performs efficiently in terms of speed and memory on large databases.