Volume 8, Issue 3, April 2017

A Novel Model of Software Process Improvements for Small and Medium Scale Enterprises by using the Big Data Analytics Approach

Muhammad Ayaz

College of Computer Science & Information Systems, Department of Computer Science, Umm Al-Qura University, Makkah Al Mukarramah, Kingdom of Saudi Arabia

Abstract– Most of the traditional organizations are not using the Big Data concept in the business process and other business activities but the rise of Big Data approach has been significantly transforming certain business enterprises. The majority of small software organizations are not adopting the existing models like CMM, CMMI, and ISO 9001 because they believe that these models are good for large organizations but fail to work in the SMEs. However, most of the software development companies around the globe are small and medium scale enterprises. The Software Process Improvement (SPI) has been recognized as an efficient way for firms to improve quality and productivity of the software they develop. Every software firm needs a well-understood and well managed software development process to improve the quality of their product. The problems with the traditionally SMEs is that they are using only internal data for decision making and other business activities like sale, inventory and shipments. However, to improve performance, we need to use data which is available inside and outside the organization. We propose the Big Data Analytics approach in our model to use data from different data sources to improve the performance of the business process in the SMEs.

Keywords— Business Intelligence, Software Quality, Small and Medium Enterprises, Software Process, Improvement and Capability Maturity

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Empirical Study of Long Parameter List Code Smell and Refactoring Tool Comparison

Saira Moin u din, Fatima Iqbal, Hafiz Ali Hamza and Sajida Fayyaz

Department of Computer Science, University of Lahore, Pakistan (Sargodha Campus)
University of South Asia Lahore, Pakistan

Abstract– The main focus of software engineering industry is performance, security and reliability which are difficult to manage in software at the same time. The main hurdle to achieve this is code smells that hinders the performance of software. Martin Fowler defined 22 bad code smells and their treatment is termed as refactoring. Refactoring improves the overall structure of the software and results an overall increase in quality of a software. There are different tools in market for code smell detection and refactoring but none of the tools can treat all code smells. We have presented a java based prototype BSDR for bad smell detection and refactoring based on the principal of human mental theory. We have compared the results of BSDR with two market oriented tools, Checkstyle and PMD (source code analyzer) against a code smell named Long Parameter List. The results show that PMD and Checkstyle show almost same results but BSDR shows little bit better results as compare to both which can be better in future.

Keywords— Code Smells, Refactoring, BSDR (Bad Smell Detection and Refactoring), Long Parameter List, Checkstyle, and PMD

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Identification of Heavy Metals in Some Water Sources in Khartoum State Using Laser Induced Breakdown Spectroscopy

Nafie A. Almuslet and EhessanB. Mirgani

Institute of Laser, Sudan University of Science and Technology, Khartoum, Republic of Sudan

Abstract– Laser Induced Breakdown Spectroscopy (LIBS) is an atomic emission spectroscopy that can analyze any sample successfully and can be applied to gas, liquid, and solid samples. It can provide nonintrusive, qualitative and quantitative measurement of elements in various test environments. Due to rapid industrial growth, environmental pollution has increased tremendously over the years especially with heavy metals. This study was designed to use LIBS technique to identify the heavy elements in some water sources in Khartoum State. Four water samples collected from different locations in Khartoum were irradiated by Q-switched Nd: YAG laser to produce its plasma. The emission spectra of the plasma were collected via optical fiber and analyzed using the data base of National Institute of Standard Technology. The analysis of the spectra showed considerable amounts of (Ni, As, Ru, Th, Zr, Tb, Eu, Li, I, Cu, Xe, K, He, Ne, Cs, Hg, Cr, Tl, Cl, Na and Fe) elements in addition to (Ni+1, As+1, Th+1, Th+2, Zr+1, Cs+1, Cs+2, Cr+1, Cr+2 , Tl+1, Tl+2, Fe+1 and Fe+2) ions. The analysis of the four water samples led to efficient detection of different heavy metals using LIBS technique.

Keywords— LIBS, Water Sources in Khartoum, Analysis and Heavy Metals

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Efficient Dual Nature Round Robin CPU Scheduling Algorithm: A Comparative Analysis

Sajida Fayyaz, Hafiz Ali Hamza and Saira Moin U Din and Iqra

Department of Computer Science, University of Lahore (Sargodha Campus), Pakistan

Abstract– Operating system is the soul computer which helps the CPU in scheduling of all the processes that are performed. There are many CPU scheduling algorithms including the “First Come First Serve” (FCFS), “Shortest Job First” (SJF), “Round Robin” (RR) and SJF with priority. All these above mention algorithms are now in use for providing satisfactory results regarding CPU utilization. Using all these algorithms we have proposed the new algorithm that is named as EDNRR (Efficient Dual Nature round Robin). Our study used the concept of RR, Improved Shortest Remaining Burst Round Robin (ISRBRR) and Shortest Remaining Burst Round Robin (SRBRR). The objective of proposed algorithm is to reduce the starvation, total turnaround time and wait time using RR by setting the time quantum in the increasing order and decreasing order. The performance of CPU is based on the scheduling of processes, according to the calculation results; the wait time of processor is reduced up to 20%.

Keywords— Scheduling Algorithm, Operating System, ENDRR (Efficient Dual Nature Round Robin), SRBRR (Small Remaining Burst Round Robin), RR (Round Robin), FCFS and SJF (Shortest Job First)

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Machine Learning Techniques for Sentiment Analysis: A Review

Munir Ahmad, Shabib Aftab, Syed Shah Muhammad and Sarfraz Ahmad

Department of Computer Science, Virtual University of Pakistan

Abstract– Social media platforms and micro blogging websites are the rich sources of user generated data. Through these resources, users from all over the world express and share their opinions about a variety of subjects. The analysis of such a huge amount of user generated data manually is impossible, therefore an effective and intelligent technique is needed which can analyze and provide the polarity of this textual data. Multiple tools and techniques are available today for automatic sentiment classification for this user generated data. Mostly, three approaches are used for this purpose Lexicon based techniques, Machine Learning based techniques and hybrid techniques (which combines lexicon based and machine learning based approach). Machine Learning approach is effective and reliable for opinion mining and sentiment classification. Many variants and extensions of machine learning techniques and tools are available today. The purpose of this study is to explore the different machine learning techniques to identify its importance as well as to raise an interest for this research area.

Keywords— Machine Learning, Sentiment Analysis, Opinion Mining, Social Media and Polarity Detection

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A Self Adaptive Interface Design System based on Personality Aesthetics for E-Learning

Rida Zahra, Shareena Zafar and Nawal Irum

Department of Computer Science, University of Lahore

Abstract– In the current era of technology, people who are busy with the hectic life and yet want to continue their learning phase, prefer E-learning.. For this, the interfaces of the software and web based systems are quite imperative. The modern man of this technology era is more concerned with the looks and user friendly interface. Considering the learning advantages of technologies and web, we find several tutorials and teaching platforms, where the UI design, look, and feel affects the cognition as well as the way of learning and User Experience UX. Several studies show that metrics of good aesthetics, interactive designs, and good typography lead towards a good design. So, for proper reflection of society in research, it is needed to consider everybody's personalized interests as well. Having an unpleasant interface may lose users. This counts as a reason to analyse more ways to enhance the learning process. This paper would focus on analysing the interface design of E-learning systems as per the user’s satisfaction. This is done by considering user’s own aesthetic sense for their better performance and enhanced learning rate over tutorial based systems. Moreover, the effect of Self Adaption and User Modelling on E-learning and tutorial systems is also discussed.

Keywords— E-Learning, Human Computer Interaction, UI (User Interface), UX (User Experience), User Modeling and Aesthetics Design

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