Nor Hawaniah Zakaria

The Important Role of People, Process and Technology during Software Project Requirement

The need of organisation is to enhance the services through Malaysian Public Sector system application. This paper described the role of people, process and technology during software project requirement in Malaysian Public Sector. We have done the survey to the IT Manager in Malaysian Public Sector. The Public Sector IT Manager contributed their working experienced and knowledge for the survey. The purpose of the survey is to determine the RE practice that implemented during software project requirement. We also determine the role for people, process and technology during the software project requirement. The result shows that 79.7 percent of IT Manager in Malaysia Public Sector has roles in software project requirement such as project leader, project manager and team leader. Some of them have to be a consultant to other agency or organization. They play their roles in business rules, business process indirectly.

Predicting Software Reliability with a Novel Neural Network Approach

With the application of software systems in variety critical field the complexity level of software has increased, so software reliability has become more and more difficult to guarantee. To overcome human power and time limitation, researchers have focused on a soft computing approach. Nevertheless, the new techniques - especially NN - have some problems, like no solid mathematical foundation for analysis, trap in local minima and a convergence problem. This paper proposed a model to predict software reliability by hybridizing the Multi-Layer Perceptron neural network (MLP) and Imperialist Competitive algorithm (ICA). This model has solved most of the previous problems, such as the convergence problem, requiring a large amount of data, and it can be applied in complex systems. Numerical results show that both the training and testing stages of proposed approach have greater accuracy in predicting the number of software failures compared to the existing approaches.

A Comprehensive Study on Opinion Mining Features and Their Applications

E-shopping is a modern approach to buy or sell products/services and becomes so popular with growing the Internet. So, opinion mining becomes a very important concept in data mining area as researchers and business usually need to know about the overall sentiment of viewpoint of people about desired phenomena. Opinion mining is the fundamental phase for variety data mining applications such as opinion summarization, recommendation system and opinion spam detection. To achieve the best results of opinion mining, we need to use the proper set of features for classification and clustering. In this paper, we do comprehensive investigation on various types of features exploited in variety sub-branches of opinion mining domain. We present the most frequent features sets includes structural, linguistic and relation-based features as a comprehensive reference for further opinion mining research. The results proved that using multiple types of features improve the accuracy of opinion mining applications.