Imran Ghani

Quality of service approaches in cloud computing

Context: Cloud computing is a new computing technology that provides services to consumers and businesses. Due to the increasing use of these services, the quality of service (QoS) of cloud computing has become an important and essential issue since there are many open challenges which need to be addressed related to trust in cloud services. Many research issues have been proposed in QoS approaches in the cloud computing area.Objective: The aim of this study is to survey current research on QoS approaches in cloud computing in order to identify where more emphasis should be placed in both current and future research directions.Method: A systematic mapping study was performed to find the related literature, and 67 articles were selected as primary studies that are classified in relation to the focus, research type and contribution type.Result: The majority of the articles are of the validation research type (64%). Infrastructure as a service (48%) was the largest research focus area, followed by software as a service (36%). The majority of contributions concerned methods (48%), followed by models (32%).Conclusion: The results of this study confirm that QoS approaches in cloud computing have become an important topic in the cloud computing area in recent years and there remain open challenges and gaps which require future research exploration. In particular, tools, metrics and evaluation research are needed in order to provide useful and trustworthy cloud computing services that deliver appropriate QoS. 67 primary studies addressed QoS in cloud computing.The largest of studies discussed validation.The majority of studies considered infrastructure as service.Most studies focused on methods or models.QoS approaches require further research.

A review on software development security engineering using Dynamic System Method (DSDM)

methodology such as Scrum, Extreme Programming (XP), Feature Driven Development (FDD) and the Dynamic System Development Method (DSDM) have gained enough recognition as efficient development process by delivering software fast even under the time constrains. However, like other agile methods DSDM has been criticized because of unavailability of security element in its four phases. In order to have a deeper look into the matter and discover more about the reality, we conducted a literature review. Our findings highlight that, in its current form, the DSDM does not support developing secure software. Although, there are a few researches on this topic about Scrum, XP and FDD but, based on our findings, there is no research on developing secure software using DSDM. Thus, in our future work we intend to propose enhanced DSDM that will cater the security aspects in software development.

Semantic Computing for Big Data: Approaches, Tools, and Emerging Directions (2011-2014)

The term “big data” has recently gained widespread attention in the field of information technology (IT). One of the key challenges in making use of big data lies in finding ways to uncover relevant and valuable information. The high volume, velocity, and variety of big data hinder the use of solutions that are available for smaller datasets, which involve the manual interpretation of data. Semantic computing technologies have been proposed as a means of dealing with these issues, and with the advent of linked data in recent years, have become central to mainstream semantic computing. This paper attempts to uncover the state-of-the-art semantics-based approaches and tools that can be leveraged to enrich and enhance todays big data. It presents research on the latest literature, including 61 studies from 2011 to 2014. In addition, it highlights the key challenges that semantic approaches need to address in the near future. For instance, this paper presents cutting-edge approaches to ontology engineering, ontology evolution, searching and filtering relevant information, extracting and reasoning, distributed (web-scale) reasoning, and representing big data. It also makes recommendations that may encourage researchers to more deeply explore the applications of semantic technology, which could improve the processing of big data. The findings of this study contribute to the existing body of basic knowledge on semantics and computational issues related to big data, and may trigger further research on the field. Our analysis shows that there is a need to put more effort into proposing new approaches, and that tools must be created that support researchers and practitioners in realizing the true power of semantic computing and solving the crucial issues of big data.

Cross Domain Recommender Systems: A Systematic Literature Review

Cross domain recommender systems (CDRS) can assist recommendations in a target domain based on knowledge learned from a source domain. CDRS consists of three building blocks: domain, user-item overlap scenarios, and recommendation tasks. The objective of this research is to identify the most widely used CDRS building-block definitions, identify common features between them, classify current research in the frame of identified definitions, group together research with respect to algorithm types, present existing problems, and recommend future directions for CDRS research. To achieve this objective, we have conducted a systematic literature review of 94 shortlisted studies. We classified the selected studies using the tag-based approach and designed classification grids. Using classification grids, it was found that the category-domain contributed a maximum of 62%, whereas the time domain contributed at least 3%. User-item overlaps were found to have equal contribution. Single target domain recommendation task was found at a maximum of 78%, whereas cross-domain recommendation task had a minor influence at only 10%. MovieLens contributed the most at 22%, whereas Yahoo-music provided 1% between 29 datasets. Factorization-based algorithms contributed a total of 37%, whereas semantics-based algorithms contributed 6% among seven types of identified algorithm groups. Finally, future directions were grouped into five categories.

E-Learning Recommender Systems Based on Goal-Based Hybrid Filtering

This research work is based on the thesis contribution by proposing the goal-based hybrid filtering approach in e-learning recommender systems (eLearningRecSys). The proposed work has been used to analyze the personalized similarities between learners profile preferences collaboratively. The proposed work consists of two hybridizations: the first hybridization has been made with content-based filtering and collaborative features to overcome the new-learners zero-rated profile recommendations issue; the second hybridization has been done with collaborative filtering and k-neighborhood scheme features to improve the average-learners low-rated profile recommendations issue. Therefore, the proposed goal-based hybrid filtering approach that hybridized content-based filtering, collaborative filtering and k-neighborhood features simultaneously works on both types of learners profiles recommendation issues in e-learning environments. The experiments in the proposed work are done using the famous “MovieLens” dataset, while the evaluation of experimental results has been performed with mean of precision 83.44% and mean of recall 85.22%, respectively. t-test result shows the probability difference value of 0.29 between the proposed hybrid approach and the evaluated literature work. The results demonstrate the effectiveness of the proposed hybrid recommender systems in e-learning scenarios.

Developing Secure Websites Using Feature Driven Development (FDD): A Case Study

Agile processes, like Feature Driven Development (FDD), Scrum and Extreme Programming (XP), have been criticized for not providing a suitable framework for building secure software. In order to find the real-life issues, this case study was initiated to investigate whether the existing FDD can withstand requirements change and software security altogether. The case study was performed in controlled environment – in a course called Application Development—a four credit hours course at UTM. The course began by splitting up the class to seven software development groups and two groups were chosen to implement the existing process of FDD. After students were given an introduction to FDD, they started to adapt the processes to their proposed system. Then students were introduced to the basic concepts on how to make software systems secure. Though, they were still new to security and FDD, however, this study produced a lot of interest among the students. The students seemed to enjoy the challenge of creating secure system using FDD model.

Effective Regression Test Case Selection: A Systematic Literature Review

Regression test case selection techniques attempt to increase the testing effectiveness based on the measurement capabilities, such as cost, coverage, and fault detection. This systematic literature review presents state-of-the-art research in effective regression test case selection techniques. We examined 47 empirical studies published between 2007 and 2015. The selected studies are categorized according to the selection procedure, empirical study design, and adequacy criteria with respect to their effectiveness measurement capability and methods used to measure the validity of these results. The results showed that mining and learning-based regression test case selection was reported in 39% of the studies, unit level testing was reported in 18% of the studies, and object-oriented environment (Java) was used in 26% of the studies. Structural faults, the most common target, was used in 55% of the studies. Overall, only 39% of the studies conducted followed experimental guidelines and are reproducible. There are 7 different cost measures, 13 different coverage types, and 5 fault-detection metrics reported in these studies. It is also observed that 70% of the studies being analyzed used cost as the effectiveness measure compared to 31% that used fault-detection capability and 16% that used coverage.

Evaluation of the Challenges of Developing Secure Software Using the Agile Approach

A set of challenges of developing secure software using the agile development approach and methods are reported in the literature. This paper reports about a systematic literature review to identify these challenges and evaluate the causes of each of these challenges, with respect to the agile values, the agile principles, and the security assurance practices. The authors identified in this study 20 challenges, which are reported in 28 publications. They found that 14 of these challenges are valid and 6 are neither caused by agile values and principles, nor by the security assurance practices. The authors also found that 2 of the valid challenges are related to the software development life-cycle, 4 are related to incremental development, 4 are related to security assurance, 2 are related to awareness and collaboration, and 2 are related to security management. These results justify the need for research to make developing secure software smooth.

Interaction-based collaborative recommendation: A personalized learning environment (PLE) perspective

In this modern era of technology and information, e-learning approach has become an integral part of teaching and learning using modern technologies. There are different variations or classification of e-learning approaches. One of notable approaches is Personal Learning Environment (PLE). In a PLE system, the contents are presented to the user in a personalized manner (according to the user’s needs and wants). The problem arises when a new user enters the system, and due to the lack of information about the new user’s needs and wants, the system fails to recommend him/her the personalized e-learning contents accurately. This phenomenon is known as cold-start problem. In order to address this issue, existing researches propose different approaches for recommendation such as preference profile, user ratings and tagging recommendations. In this research paper, the implementation of a novel interaction-based approach is presented. The interaction-based approach improves the recommendation accuracy for the new-user cold-start problem by integrating preferences profile and tagging recommendation and utilizing the interaction among users and system. This research work takes leverage of the interaction of a new user with the PLE system and generates recommendation for the new user, both implicitly and explicitly, thus solving new-user cold-start problem. The result shows the improvement of 31.57% in Precision, 18.29% in Recall and 8.8% in F1-measure.

Questionnaire based Approach to Measure Security in Requirement Engineering

The aim of this paper is to measure security in requirement engineering using questionnaire based approach. The questionnaire is applied in the four stages of requirement engineering (Elicitation, analyses, validation, management). The questionnaire based approach is composing of three main parts . First the security questions part . Second the evaluation part which should be filled by the stakeholders . Third the assessment part . Finally A case study conducted to apply Questionnaire based approach and to measure security in requirement engineering.