Saiful Adli Ismail

Predicting effort for requirement changes during software development

In any software development life cycle, requirement and software changes are inevitable. One of the factors that influences the effectiveness of the change acceptance decision is the accuracy of the effort prediction for requirement changes. There are two current models that have been widely used to predict rework effort for requirement changes which are algorithmic and non-algorithmic models. The algorithmic model is known for its formal and structural way of prediction and best suited for Traditional software development methodology. While non-algorithmic model is widely adopted for Agile software development methodology of software projects due to its easiness and requires less work in term of effort predictability. Nevertheless, none of the existing effort prediction models for requirement changes are proven to suit both, Traditional and Agile software development methodology. Thus, this paper proposes an algorithmic-based effort prediction model for requirement changes that uses change impact analysis method which is applicable for both Traditional and Agile software development methodologies. The proposed model uses a current selected change impact analysis method for software development phase. The proposed model is evaluated through an extensive experimental validation using case study of six real Traditional and Agile methodologies software projects. The evaluation results confirmed a significance accuracy improvement of the proposed model over the existing approaches for both Traditional and Agile methodologies.

New security authentication mechanisms in grid computing web environment

The fundamental of the authentication of grid computing web-based environment is to ensure the security of local and remote entities in the environment. The existing authentication mechanisms mostly use public key infrastructure (PKI), but the potential PKI vulnerability and implementation constrains are still cannot be avoid. In order to satisfy the security needs of grid computing web environment, we design new authentication mechanisms, which aim to avoid uploading the public/private key pair to a web portal grid environment through world wide web (www).

An Empirical Study with Function Point Analysis for Software Development Phase Method

It is important to know the actual size and complexity of the software before predicting the amount of effort required to be implemented. Two most common methods used for software size estimation are: (i) Source Lines of Code (SLOC) and (ii) Function Point Analysis (FPA). Estimating the size of a software with SLOC method is only possible once the process of coding is completed. On the other hand, estimating software size with FPA method is possible in early phases of Software Development Life Cycle (SDLC). However, one main challenge from the viewpoint of software development phase, is the presence of inconsistent states of software artifacts i.e. some of the classes are completely developed, some are partially developed, and some are not developed yet. Therefore, this research is using the new developed model i.e. Function Point Analysis for Software Development Phase (FPA-SDP) in an empirical study to overcome this challenge. The results of FPA-SDP model can help software project managers in: (i) knowing the inconsistent states of software artifacts (ii) estimating the actual size of a change request with its complexity level for software development phase.

Analyzing Traffic Accident and Casualty Trend Using Data Visualization

Motor vehicle is the backbone of the modern transportation system worldwide. However, the excessive number of motor vehicles tend to cause traffic accidents leading to numerous casualties. Analyzing existing works on this area, this study has identified prime reasons behind traffic accidents and casualties. They include driving over the speed limit, Age of drivers and pedestrians, environmental condition, location and road types. It has also reviewed and identified several data visualization methods and visualization techniques that have been proposed by many researchers. The objective of this research endeavour is to identify the factors behind traffic accidents, determine the techniques that are used to visualize data, develop a dashboard using data visualization tools to visualize traffic accident trend and to evaluate the functionality of the dashboard which is developed on United Kingdom’s (UK) traffic accident dataset from 2014 to 2016. Upon performing data cleaning, pre-processing and filtering, the raw data has been converted into cleaned, filtered and processed data to create a coherent and properly linked data model. Then, using the Power BI visualization tool, various interactive visualizations have been produced that illustrated several significant trends in accident and casualties. The visualization trend revealed that between 2014 to 2016, majority of the accidents in the UK occurred in the urban area, in the single carriageway, on the dry road surface, under the daylight with fine weather, and when the speed limit was below 30 mph. This research may assist UK’s traffic management authority to identify the underlying factors behind the traffic accident and to detect the traffic accident and casualty trend in order to take necessary steps to reduce casualties in traffic accidents.

Data Quality Issues in Big Data: A Review

Data with good quality has precedence when analyzing and using big data to deduce value from such tremendous volume of data in today’s business environments. Decisions and insights derived from poor data has a negative and unpredictable consequences to organizations. At present, due to the lack of comprehensive and intensive research in the field of data quality, especially large data, there is an urgent need to address this issue by researchers to reach the optimal way to estimate and evaluate the quality of large data. Thus, enabling institutions to make rational decisions based on evaluation outputs. In this paper, the current research on the quality of large data was reviewed and summarized by exploring the basic characteristics of large data. The main challenges facing the quality of information were also discussed in the context of large data. Some of the initiatives suggested by the researchers to evaluate the quality of the data have been highlighted. Finally, we believe that the results of these reviews will enhance the conceptual measurements of the large data quality and produce a concrete groundwork for the future by creating an integrated data quality assessment and evaluation models using the suitable algorithms.

The challenges of Extract, Transform and Loading (ETL) system implementation for near real-time environment

Organization with considerable investment into data warehousing, the influx of various data types and forms requires certain ways of prepping data and staging platform that support fast, efficient and volatile data to reach its targeted audiences or users of different business needs. Extract, Transform and Load (ETL) system proved to be a choice standard for managing and sustaining the movement and transactional process of the valued big data assets. However, traditional ETL system can no longer accommodate and effectively handle streaming or near real-time data and stimulating environment which demands high availability, low latency and horizontal scalability features for functionality. This paper identifies the challenges of implementing ETL system for streaming or near real-time data which needs to evolve and streamline itself with the different requirements. Current efforts and solution approaches to address the challenges are presented. The classification of ETL system challenges are prepared based on near real-time environment features and ETL stages to encourage different perspectives for future research.

Authentication mechanisms in a control grid computing environment using identity based identification (IBI)

Highly critical authentication in Grid Computing systems required secured data transmission to be protected from unintended user and free modification. The process of protecting data from unauthorized user known as cryptography while protecting the user data from modification can be achieved through verification process. However, most of the grid computing system is still using public key infrastructure (PKI) as an industry standard and certificate authority (CA) server to authenticate users to access the grid resources. Several works have been done enhancing RSA algorithm for grid authentication computing system. Though, none of the work has addressed specifically how to avoid the vulnerability. This paper proposed an alternative authentication mechanism using Identity based Identification (IBI) algorithm with a verification process to ensure the user identification, and carry out the experiment in the GridSim toolkit simulator.

Energy harvesting in wireless sensor networks: A survey

With the emergence of Internet-of-Things (IoT) technologies, the application of Wireless Sensor Network (WSN) has become pervasive in our physical environment. These technologies rely on the WSN to capture environmental data and transmit it through the network to the specified location or storage. Given the massive data that captured by the WSN and the data transmission that happen in the process, there is a challenge for any developers and researchers in WSN in terms of a continuous operation of the WSN device. This is because WSNs are battery-powered and often deployed in a remote location that is hard to access for a battery replacement. The purpose of this paper is to survey the energy harvesting for improving the lifetime of the wireless sensor networks powered by the environmental energy. Energy harvesting takes advantages of the stray energy from the environment as the sustainable source of power.

HMAC Authentication Mechanisms in a Grid Computing Environment Using Gridsim Toolkit

Recently the authentication of grid computing environment has been to secure the security of local and remote entities in the surrounding. The existing authentication mechanisms mostly use public key infrastructure (PKI), but the potential PKI vulnerability and implementation constraints are still cannot be a void. In order to satisfy the security needs of grid computing environment, this paper proposes an authentication mechanism using HMAC (a hash, MAC) to secure the user identification that bears the characteristic of public key replacement attack resistance, which run the experiment in the GridSim toolkit simulator.

A change impact analysis tool: integration between static and dynamic analysis techniques

Accepting too many software change requests could contribute to expense and delay in project delivery. On the other hand rejecting the changes may increase customer dissatisfaction. Software project management might use a reliable estimation on potential impacted artifacts to decide whether to accept or reject the changes. In software development phase, an assumption that all classes in the class artifact are completely developed is impractical compared to software maintenance phase. This is due to some classes in the class artifact are still under development or partially developed. This paper is a continuous effort from our previous work on combining between static and dynamic analysis techniques for impact analysis. We have converted the approach to an automated tool and call it a CIAT (Change Impact Analysis Tool). The significant achievements of the tool are demonstrated through an extensive experimental validation using several case studies. The experimental analysis shows improvement in the accuracy over current impact analysis results.