Dayang Norhayati Abang Jawawi

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 PSO-based model to increase the accuracy of software development effort estimation

Development effort is one of the most important metrics that must be estimated in order to design the plan of a project. The uncertainty and complexity of software projects make the process of effort estimation difficult and ambiguous. Analogy-based estimation (ABE) is the most common method in this area because it is quite straightforward and practical, relying on comparison between new projects and completed projects to estimate the development effort. Despite many advantages, ABE is unable to produce accurate estimates when the importance level of project features is not the same or the relationship among features is difficult to determine. In such situations, efficient feature weighting can be a solution to improve the performance of ABE. This paper proposes a hybrid estimation model based on a combination of a particle swarm optimization (PSO) algorithm and ABE to increase the accuracy of software development effort estimation. This combination leads to accurate identification of projects that are similar, based on optimizing the performance of the similarity function in ABE. A framework is presented in which the appropriate weights are allocated to project features so that the most accurate estimates are achieved. The suggested model is flexible enough to be used in different datasets including categorical and non-categorical project features. Three real data sets are employed to evaluate the proposed model, and the results are compared with other estimation models. The promising results show that a combination of PSO and ABE could significantly improve the performance of existing estimation models.

Aspect-oriented model-driven code generation: A systematic mapping study

Context: Model-driven code generation is being increasingly applied to enhance software development from perspectives of maintainability, extensibility and reusability. However, aspect-oriented code generation from models is an area that is currently underdeveloped. Objective: In this study we provide a survey of existing research on aspect-oriented modeling and code generation to discover current work and identify needs for future research. Method: A systematic mapping study was performed to find relevant studies. Classification schemes have been defined and the 65 selected primary studies have been classified on the basis of research focus, contribution type and research type. Results: The papers of solution proposal research type are in a majority. All together aspect-oriented modeling appears being the most focused area divided into modeling notations and process (36%) and model composition and interaction management (26%). The majority of contributions are methods. Conclusion: Aspect-oriented modeling and composition mechanisms have been significantly discussed in existing literature while more research is needed in the area of model-driven code generation. Furthermore, we have observed that previous research has frequently focused on proposing solutions and thus there is need for research that validates and evaluates the existing proposals in order to provide firm foundations for aspect-oriented model-driven code generation.

A flexible method to estimate the software development effort based on the classification of projects and localization of comparisons

The estimation of software development effort has been centralized mostly on the accuracy of estimates through dealing with heterogeneous datasets regardless of the fact that the software projects are inherently complex and uncertain. In particular, Analogy Based Estimation (ABE), as a widely accepted estimation method, suffers a great deal from the problem of inconsistent and non-normal datasets because it is a comparison-based method and the quality of comparisons strongly depends on the consistency of projects. In order to overcome this problem, prior studies have suggested the use of weighting methods, outlier elimination techniques and various types of soft computing methods. However the proposed methods have reduced the complexity and uncertainty of projects, the results are not still convincing and the methods are limited to a special domain of software projects, which causes the generalization of methods to be impossible. Localization of comparison and weighting processes through clustering of projects is the main idea behind this paper. A hybrid model is proposed in which the software projects are divided into several clusters based on key attributes (development type, organization type and development platform). A combination of ABE and Particle Swarm Optimization (PSO) algorithm is used to design a weighting system in which the project attributes of different clusters are given different weights. Instead of comparing a new project with all the historical projects, it is only compared with the projects located in the related clusters based on the common attributes. The proposed method was evaluated through three real datasets that include a total of 505 software projects. The performance of the proposed model was compared with other well-known estimation methods and the promising results showed that the proposed localization can considerably improve the accuracy of estimates. Besides the increase in accuracy, the results also certified that the proposed method is flexible enough to be used in a wide range of software projects.

Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm

Abstract This paper presents a physics-inspired metaheuristic optimization algorithm, known as Electromagnetic Field Optimization (EFO). The proposed algorithm is inspired by the behavior of electromagnets with different polarities and takes advantage of a nature-inspired ratio, known as the golden ratio. In EFO, a possible solution is an electromagnetic particle made of electromagnets, and the number of electromagnets is determined by the number of variables of the optimization problem. EFO is a population-based algorithm in which the population is divided into three fields (positive, negative, and neutral); attraction–repulsion forces among electromagnets of these three fields lead particles toward global minima. The golden ratio determines the ratio between attraction and repulsion forces to help particles converge quickly and effectively. The experimental results on 30 high dimensional CEC 2014 benchmarks reflect the superiority of EFO in terms of accuracy and convergence speed over other state-of-the-art optimization algorithms.

Increasing the accuracy of software development effort estimation using projects clustering

Software development effort is one of the most important metrics that must be correctly estimated in software projects. Analogy-based estimation (ABE) and artificial neural networks (ANN) are the most popular methods used widely in this field. These methods suffer from inconsistent and irrelevant projects that exist in the software project datasets. In this paper, a new hybrid method is proposed to increase the accuracy of development effort estimation based on the combination of fuzzy clustering, ABE and ANN methods. In the proposed method, the effect of irrelevant and inconsistent projects on estimates is decreased by designing a new framework, in which all the projects are clustered. The quality of training in ANN and the consistency of historical data in ABE are improved using the proposed framework. Two large and real datasets are utilised in order to evaluate the performance of the proposed method and the obtained results are compared to eight other estimation methods. The promising results showed that the proposed method outperformed the other methods on both datasets. The performance metrics of mean magnitude of relative error (MMRE) and the percentage of the prediction (PRED) (0.25) have been improved by average of 51 and 127% in the first dataset, as well as 52 and 94% in the second dataset.

Enhancements of PECOS Embedded Real-Time Component Model for Autonomous Mobile Robot Application

Recent@, Component-Based Sofiare Enxineering (CBSE) has becoming a poptrlar approach.for developing embedded sofiare. In CBSE, a component model is required to specifY the standards and conventions imposed on developers of components. Indushial cornponent models such as CORBA, CUM and JavaBeans are general!^ not stritable for embedded real-time (ERT) Vstenis. Consequently, a number of component models slritahle for CBSE ofERT sofiare such 0.7 PBO, Koala, PECOS and ReFIex are introduced. Assessments of the PECOS component model were conducted to evaltrate the suitabiliw of PECOS component model for adoption in CRSE of autonomous mobile-robot (AMR) software. The as.~e.ssments emphasize on three requirentent.y: facilitates predictable real-time performance, support for resource constraint systems, and support plarfonn-independent implementation. Three enhancemenlr were proposed for the PECOS component model. These enhancements were implemented on a real m-wheeled mobile robot. and resul~f show that, the PECOS cornponent model together with the proposed modifications can generate application siritable for resource constrained AMR vstem.r, the new mapping of component behavior to tash process can be used to guarantee the nm-time predictabili(v and perjormance, and the new irnplenientation pamework proposed enable plarform independent development of AMR sofiare.

A Component-Oriented Programming for Embedded Mobile Robot Software

Applying software reuse to many Embedded Real-Time (ERT) systems poses significant challenges to industrial software processes due to the resource-constrained and real-time requirements of the systems. Autonomous Mobile Robot (AMR) system is a class of ERT systems, hence, inherits the challenge of applying software reuse in general ERT systems. Furthermore, software reuse in AMR systems is challenged by the diversities in terms of robot physical size and shape, environmental interaction and implementation platform. Thus, it is envisioned that component-based software engineering will be the suitable way to promote software reuse in AMR systems with consideration to general requirements to be self-contained, platform-independent and real-time predictable. A framework for component-oriented programming for AMR software development using PECOS component model is proposed in this paper. The main features of this framework are: (1) use graphical representation for components definition and composition; (2) target C language for optimal code generation with resource-constrained micro-controller; and (3) minimal requirement for run-time support. Real-time implementation indicates that, the PECOS component model together with the proposed framework is suitable for resource constrained embedded AMR systems software development.

Service based meta-model for the development of distributed embedded real-time systems

The development complexity of Distributed Embedded Real-Time Systems (DERTS) can be reduced by the use of Service-Oriented Computing (SOC). However, the existing modeling methods allow modeling of either DERTS or SOC concepts and there is a lack of meta-model for Service-Oriented development of DERTS. This paper proposes a service-based meta-model for DERTS, along with the constraints of the elements of the meta-model. A Smart Home case study was designed to validate the meta-model. This meta-model could not only be beneficial for Service-Oriented development of DERTS, but can also be used at the Platform Independent Model (PIM) level of MDD.

LMES: A localized multi-estimator model to estimate software development effort

Accurate estimation of software development effort is strongly associated with the success or failure of software projects. The clear lack of convincing accuracy and flexibility in this area has attracted the attention of researchers over the past few years. Despite improvements achieved in effort estimating, there is no strong agreement as to which individual model is the best. Recent studies have found that an accurate estimation of development effort in software projects is unreachable in global space, meaning that proposing a high performance estimation model for use in different types of software projects is likely impossible. In this paper, a localized multi-estimator model, called LMES, is proposed in which software projects are classified based on underlying attributes. Different clusters of projects are then locally investigated so that the most accurate estimators are selected for each cluster. Unlike prior models, LMES does not rely on only one individual estimator in a cluster of projects. Rather, an exhaustive investigation is conducted to find the best combination of estimators to assign to each cluster. The investigation domain includes 10 estimators combined using four combination methods, which results in 4017 different combinations. ISBSG, Maxwell and COCOMO datasets are utilized for evaluation purposes, which include a total of 573 real software projects. The promising results show that the estimate accuracy is improved through localization of estimation process and allocation of appropriate estimators. Besides increased accuracy, the significant contribution of LMES is its adaptability and flexibility to deal with the complexity and uncertainty that exist in the field of software development effort estimation.