Jafreezal Jaafar

A rule-based model for software development team composition: Team leader role with personality types and gender classification

Abstract Context Recent studies have established the fact that the supply of handy and successful software has decreased to 6%. The past studies have also attributed this supply failure to software development team composition factor. To overcome this problem, it is also suggested in the past studies that the soft skills of team member must be considered along with the hard skills. Objective Keeping in view this problem, this study aimed to look for in-depth understanding of team-lead role with personality types of member. This study also included gender to see its diverting impact on personality types and job role, since past studies have also raised many issues pertinent to these two variables. Method This study used the experimented data to develop the rule-based model for software development team composition by keeping gender as major effecting variable with personality. There were three independent predictor variables: Team leader role, Personality types, and Gender; and one outcome dependent variable: team performance. Additionally, personality types of team members were measured by using Myers–Briggs Type Indicator(MBTI) instrument. This study divided the experiments into two stages. The first stage was descriptive examination of factual figures of data for model development. Whereas, the second stage was predictive experiments of data for developing the model. Results The findings revealed that each gender emerged compatible with different types of personality for the same role. For instance, descriptive analysis part of this research highlighted that feeling(F) personality males were appropriate for team leader role, on another hand, thinking(T) personality females were suitable for the team lead role. Conclusion The conclusion can be drawn with the claim that the personality types of software development team roles fluctuate by gender type. Besides, this study revealed and ensured that gender should be kept in the consideration when composing teams based on personality types.

ARIMA based Interval Type-2 Fuzzy Model for Forecasting

To solve the chaotic and uncertain problems, researchers are focusing on the extensions of classical fuzzy model. At present Interval Type-2 Fuzzy logic Systems (IT2-FLS) are extensively used after the thriving exploitation of Type-2 FLS. Fuzzy time series models have been used for forecasting stock and FOREX indexes, enrollments, temperature, disease diagnosing and weather. In this paper a hybrid fuzzy time series model is proposed that will develop an Interval type 2 fuzzy model based on ARIM A. The proposed model will use ARIM A to select appropriate coefficients from the observed dataset. IT2-FLS is utilized here for handling the uncertainty in the time series data so that it may yield a more accurate forecasting result.

Model driven security frameworks for addressing security problems of Service Oriented Architecture

Service Oriented Architecture (SOA) based on Web Services technology gained popularity because business work flows can easily be executed as an orchestration of Web Services. These Web Services are independently developed and may be internal or external. With increase in connectivity among the Web Services, security risks rise exponentially. Moreover the security requirements are not defined at organizational level rather they left until the technical level. Many security problems related to SOA applications are highlighted by different authors which if not properly managed might have serious consequences. Various Model Driven Security Frameworks are presented by different research groups to overcome the security problems of SOA based applications. In this paper we have highlighted the security problems for SOA based applications and few Model Driven Security Frameworks are presented to develop secure software applications; their working style and security goals are also discussed in the course of paper.

Monitoring web services' quality of service: a literature review

Monitoring Quality of Service (QoS) compliance is an important procedure in web service environment. It determines whether users’ expectations are met, and becomes the vital factor for them to decide whether to continue paying for the service or not. The monitoring is performed by checking the actual services performance against the QoS stated in Service Level Agreement (SLA). In relation to that, the need for monitoring vague QoS specifications in SLA has become more apparent nowadays. This paper reviews the published literature on web services QoS monitoring. A total of 60 selected articles were systematically analyzed. There were 23 of the articles selected through restrictive search criteria while the other 37 were selected based on unrestrictive search criteria. The review shows that little evidence exists on monitoring vague QoS specifications of web services. Providing ability for monitoring QoS that is specified vaguely in SLA could give new insights and implications to web services field. This paper concludes with some recommended future works to construct the theory and perform the empirical research.

Development of Web Services Fuzzy Quality Models using Data Clustering Approach

This paper presents the fuzzy clustering of web services’ quality of service (QoS) data using Fuzzy C-Means (FCM) algorithm. It was conducted based on actual QoS data gathered from the network. The work involved three data sets that represented three different QoS parameters. Each data set contained 1,500 data points. The clustering was validated using Xie-Beni index to ensure that it performed optimally. As a result, three fuzzy quality models were produced that represented the three QoS parameters. The work implies potential new findings on fuzzy-based web services’ applications, mainly in reducing computational complexity. The work also benefits the less technical-knowledgeable requestors as the fuzzy quality models can guide them to find services with realistic QoS performance. For future work, the fuzzy quality models will be employed in web services’ QoS monitoring application. They will also be equipped with an adaptive mechanism that supports the dynamic nature of web services.

Enhanced approach for developing web applications using model driven architecture

Creating of web application and corresponding information architecture is often associated with social informatics. It clearly lays at the crossing of the ICT and social sciences, especially because effective information architectures enable people to find content quickly, easily and intuitively. Model Driven Architecture (MDA) technique is initiated by the Object Management Group (OMG), based on separation of concerns. It describes the system functionality in platform independent model (PIM) and also describes the implementation of this functionality using platform specific model (PSM), where the transformation process from PIM to PSM is done automatically using QVT model transformation language. To overcome the large scale web application system complexity, model-driven web engineering is used to automate web application using models to describe web site in different abstraction level. The automation in existing web development methods could be enhanced using MDA technique. This paper provides proposed mechanism to enhance Web Site Design Method (WSDM) method by applying MDA technique. The proposed mechanism enhances WSDM from conceptual modeling approach to MDA modeling approach by profiling the conceptual model of WSDM with the new user-interest profile. This redesigned conceptual model is used as PIM model. The proposed mechanism adds generic PSM to the implementation model of WSDM. We use QVT model transformation language to automate the mapping specification from PIM to PSM. Our method is called WSDM using MDA (WSDMDA).

Load forecasting accuracy through combination of trimmed forecasts

Neural network (NN) models have been receiving considerable attention and a wide range of publications regarding short-term load forecasting have been reported in the literature. Their popularity is mainly due to their excellent learning and approximation capabilities. However, NN models suffer from the problem of forecasting performance fluctuations in different runs, due to their development and training processes. Averaging of forecasts generated by NNs has been proposed as a solution to this problem. However, this may lead to another problem as odd forecasts may significantly shift the mean resulting in large forecasting inaccuracies. This paper investigates application of a trimming method by removing the α% largest and smallest forecasts and then averaging the rest of the forecasts. A validation set is applied for selecting the best trimming amount for NN load demand forecasts. Performance of the proposed method is examined using a real world data set. Demonstrated results show that although trimmed forecasts are not the best possible ones, they are better than forecasts generated by individual NN models in almost 70% of the cases.

Electricity load and price forecasting with influential factors in a deregulated power industry

With the emergence of smart power grid and distributed generation technologies in recent years, there is need to introduce new advanced models for forecasting. Electricity load and price forecasts are two primary factors needed in a deregulated power industry. The performances of the demand response programs are likely to be deteriorated in the absence of accurate load and price forecasting. Electricity generation companies, system operators, and consumers are highly reliant on the accuracy of the forecasting models. However, historical prices from the financial market, weekly price/load information, historical loads and day type are some of the explanatory factors that affect the accuracy of the forecasting. In this paper, a neural network (NN) model that considers different influential factors as feedback to the model is presented. This model is implemented with historical data from the ISO New England. It is observed during experiments that price forecasting is more complicated and hence less accurate than the load forecasting.

Neural network ensemble: Evaluation of aggregation algorithms in electricity demand forecasting

This paper examines and analyzes different aggregation algorithms to improve accuracy of forecasts obtained using neural network (NN) ensembles. These algorithms include equal-weights combination of Best NN models, combination of trimmed forecasts, and Bayesian Model Averaging (BMA). The predictive performance of these algorithms are evaluated using Australian electricity demand data. The output of the aggregation algorithms of NN ensembles are compared with a Naive approach. Mean absolute percentage error is applied as the performance index for assessing the quality of aggregated forecasts. Through comprehensive simulations, it is found that the aggregation algorithms can significantly improve the forecasting accuracies. The BMA algorithm also demonstrates the best performance amongst aggregation algorithms investigated in this study.

Tractability method for ontology development of Semantic Web

Ontology is an explicit representation of a particular domain of interest. This paper focuses on the inherent problem of intractability (data complexity) due to data capturing for heterogeneous sources. We proposed to follow the tradition of knowledge representation and reasoning processes to develop a Semantic Web. Building query systems is the ultimate goal for this study. Conjunctive queries were adopted over ontology methods to address the data complexity. The Conjunctive queries are the form of queries in first-order logic that deal with conjunction (Λ) and existential quantification (∃) in database atomic formulae. State of the art meteorological and oceanographic domain provides the motivation for this work. We found that the querying method facilitates the tractability in reasoning and logical systems.