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الأبحاث

On the value of project productivity for early effort estimation

Authors : Mohammad Azzeh, Ali Bou Nassif, Yousef Elsheikh, Lefteris Angelis

Abstract : This paper examines the significance of learning productivity from historical data in order to estimate effort from UCP. We have conducted a set of empirical experiments using four well-known effort estimation models that use productivity and size variable as predictors. Three datasets were used to perform the experiments. Based on the findings obtained from our empirical experiments, we can summarize the following implications of our research on the software industry: 1. Using different productivity ratios for each individual project seems more effective than using a static ratio for all projects within a given software organization. 2. The different productivity ratios should be learned through a robust model from a historical dataset. 3. Karner and S&W models may work well for educational projects but not for industrial projects. 4. Environmental factors are not good metrics of productivity prediction, but they can be used as a source of data if there are no alternative methods. 5. Environmental factors, as well as technical factors must be carefully evaluated to prevent any possible ambiguity that may cause an inaccurate estimation. 6. The use of homogeneous datasets allows a slight improvement in the productivity and effort estimation for UCP models

Keywords : Use Case Points, Software Productivity, Software Effort Estimation, Software Size Measures, Regression to Mean.

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Locally weighted regression with different kernel smoothers for software effort estimation

Authors : Yousef Alqasrawi, Mohammad Azzeh, Yousef Elsheikh

Abstract : In this paper we used a more sophisticated locality approach that can mitigate these shortcomings that is Locally Weighted Regression (LWR). This method provides an efficient solution to learn from local data by building an estimation model that combines multiple local regression models in k-nearest-neighbor based model. The main factor affecting the accuracy of this method is the choice of the kernel function used to derive the weights for local regression models. This paper investigates the effects of choosing different kernels on the performance of Locally Weighted Regression of a software effort estimation problem. After comprehensive experiments with 7 datasets, 10 kernels, 3 polynomial degrees and 4 bandwidth values with a total of 840 Locally Weighted Regression variants, we found that: 1) Uniform kernel functions cannot outperform non-uniform kernel functions, and 2) kernel type, polynomial degrees and bandwidth parameters have no specific effect on the estimation accuracy. In other words, no change in bandwidth or degree values occurred with a significant difference in kernel rankings. In short, Locally Weighted Regression methods with Triweight or Triangle kernel can perform better than more complex kernels. Hence, we encourage non-uniform kernel methods as smoother function with wide bandwidth and small polynomial degree

Keywords : Effort Estimation, Locally Weighted Regression, Kernel Function, k-nearest neighbors.

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The Role of Data Pre-Processing Techniques In Improving Machine Learning Accuracy For Predicting Coronary Heart Disease

Authors : Osamah Sami, Yousef Elsheikh, Fadi Almasalha

Abstract : This paper improve the accuracy of machine learning techniques in predicting coronary heart disease using data preprocessing techniques. Data preprocessing is a technique used to improve the efficiency of a machine learning model by improving the quality of the feature. The popular Framingham Heart Study dataset was used for validation purposes. The results of the research paper indicate that the use of data preprocessing techniques had a role in improving the predictive accuracy of poorly efficient classifiers, and shows satisfactory performance in determining the risk of coronary heart disease. For example, the Decision Tree classifier led to a predictive accuracy of coronary heart disease of 91.39% with an increase of 1.39% over the previous work, the Random Forest classifier led to a predictive accuracy of 92.80% with an increase of 2.7% over the previous work, the K-Nearest Neighbor classifier led to a predictive accuracy of 92.68% with an increase of 2.58% over the previous work, the Multilayer Perceptron Neural Network (MLP) classifier led to a predictive accuracy of 92.64% with an increase of 2.64% over the previous work, and the Na¨ıve Bayes classifier led to a predictive accuracy of 90.56% with an increase of 0.66% over the previous work

Keywords : Coronary heart disease; heart; machine learning;

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On obtaining a stable vote ranking methodology for implementing e-government strategies

Authors : Yousef Elsheikh, Yousef Alqasrawi, Mohammad Azzeh

Abstract : This study will address the problem by finding a new methodology that enables to obtain a stable ranking of strategies in order to make the best strategic decision to implement the e-government program in the context under study or other similar contexts in the region. This study assumes that we can obtain a stable ranking of strategies related to implementing e-government programs when we only use the SWOT factors that produce stable ranking votes. To validate this, three main experiments with a number of proposed scenarios were developed to aggregate expert rankings on the implementation of e-government strategies using some common vote ranking methods, namely, parametric methods such as Borda, Copeland and Maximin, and algorithmic methods such as Stuart and Robust Rank Aggregation (RRA). Then, to evaluate the results of the three experimental scenarios, two new measures were proposed, namely the Similarity Index measure and the Matching Index measure. The results confirmed the validity of the assumption regarding the importance of obtaining a stable vote ranking in selecting the best strategies to ensure success in implementing e-government program in the context under study or other similar contexts in the region.

Keywords : data preprocessing; classification technique

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Toward the Conceptualization of Citizen’s Trust in Jordanian E-Government Systems: A Q-Methodology Approach

Authors : Yousef Elsheikh

Abstract : This research paper proposes a theoretical model to achieve this task. The research paper also aims to develop items to measure constructs in the theoretical proposed model. Q-Methodology has been used to validate the measurement items that have been developed. However, conducting two-phase Q-sort led to the development of a survey instrument for the theoretical proposed model with excellent reliability and validity of statistical results

Keywords : Citizen Trust; G2C; E-Government; Q-Methodology; Developing Countries

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An optimized analogy-based project effort estimation

Authors : Mohammad Azzeh, Yousef Elsheikh, Marwan Alseid

Abstract : Despite the predictive performance of Analogy-Based Estimation (ABE) in generating better effort estimates, there is no consensus on how to predict the best number of analogies, and which adjustment technique produces better estimates. This paper proposes a new adjusted ABE model based on optimizing and approximating complex relationships between features and reflects that approximation on the final estimate. The results show that the predictive performance of ABE has noticeably been improved, and the number of analogies was remarkably variable for each test project.

Keywords : Cost Estimation; Effort Estimation by Analogy; Bees Optimization Algorithm

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Learning best K analogies from data distribution for case-based software effort estimation

Authors : Mohammad Azzeh, Yousef Elsheikh

Abstract : Case-Based Reasoning (CBR) has been widely used to generate good software effort estimates. The predictive performance of CBR is a dataset dependent and subject to extremely large space of configuration possibilities. Regardless of the type of adaptation technique, deciding on the optimal number of similar cases to be used before applying CBR is a key challenge. In this paper we propose a new technique based on Bisecting k-medoids clustering algorithm to better understanding the structure of a dataset and discovering the optimal cases for each individual project by excluding irrelevant cases. Results obtained showed that understanding of the data characteristic prior prediction stage can help in automatically finding the best number of cases for each test project. Performance figures of the proposed estimation method are better than those of other regular K-based CBR methods

Keywords : Software Effort Estimation; Case-Based

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Prioritize E-Government Strategies Using SWOT-Ranked Voting Analysis Technique: The Case of Jordan

Authors : Yousef Elsheikh, Mohammad Azzeh

Abstract : l. In this paper, SWOT-Ranked voting (Borda count method) was used to achieve this task. Ranked voting theory (also called preferential voting approach) is a measure of individual interests and preferences as an aggregate towards a collective decision. Voters can rank potential candidates in order of their preferences and some aggregating rules are then used to find the winner or group of winners among the various candidates. E-government strategies have been evaluated and prioritized with the participation of 20 experts in the experiment who have experience in such large scale programs to ensure its success in the context under investigation. Kendall tau rank correlation coefficient was also used to measure the ordinal association (similarity) between the rankings that have been obtained as a result of the experiment, and those that have been observed by the experts.

Keywords : Reasoning; Adjustment Techniques.

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A Replicated Assessment of the Critical Success Factors for the Adoption of Mobile Government Services: the Case of Jordan

Authors : Yousef Elsheikh, Mohammad Hijjawi

Abstract : This research is based on a re-evaluation of the empirical results of a comprehensive study conducted by Susanto and Goodwin (2010), which concludes that there are fifteen factors that are likely to affect citizens in 25 countries around the world to adopt SMS-based e-government services, but in the context of a different country in the Arab world, namely Jordan.

Keywords : Mobile-Government, Electronic-Government, SMS Technology, Developing Countries, Jordan

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Understanding the Role of Multimodal Metaphors in E-Government Implementation: An Empirical Investigation

Authors : Mohammad Shkoukani, Yousef Elsheikh, Hesham Abusaimeh

Abstract : This paper presents the experimental results of the inclusion of multimodal metaphors in e-government interfaces in order to enhance usability on the one hand, and trust between users on the other hand to eventually lead to the adoption and use of such interfaces on a large scale in the context under investigation. In this paper, multimodal metaphors that were used: visual and auditory stimuli and avatars. These investigations were evaluated by 40 users as it consisted of two different interfaces in each experiment to demonstrate the role of multimodal metaphors in the successful implementation of e-government interface platform. The results show the positive role of multimodal metaphors in increasing the level of usability in such interactive platforms on the one hand, and on the other hand increase the users trust, and learnability while interacting with those e-government platforms. Also, the results show the validity of the research approach adopted and the way in which users interact with e-government interface platform effectively and efficiently.

Keywords : Multimodal metaphors, e-government, e-services, usability, trust, human computer interaction

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Arabic language challenges in text based conversational agents compared to the English language

Authors : Mohammad Hijjawi, Yousef Elsheikh

Abstract : This paper is not to compare between the Arabic language and the English language as natural languages. Instead, it focuses on the comparison among them in terms of their challenges in building text based Conversational Agents (CAs). A CA is an intelligent computer program that used to handle conversations among the user and the machine. Nowadays, CAs can play an important role in many aspects as this work figured. In this paper, different approaches that can be used to build a CA will be differentiated. In each approach, the comparison aspects among the Arabic and English languages will be debated with the respect to the Arabic language.

Keywords : Conversational Agents, Chatterbots, Arabic Language and English Language.

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What facilitates the delivery of citizen-centric egovernment services in developing countries: Model development and validation through structural equation modeling

Authors : Yousef Elsheikh, Mohammad Azzeh

Keywords : ICT, citizen-centric e-government services delivery, grounded theory, structural equation modeling,

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A comparative usability study on the use of auditory icons to support virtual lecturers in e-learning interfaces

Authors : M Alseid, M Azzeh, Y Elsheikh

Abstract : This paper presents further empirical investigation into the use of these supportive auditory icons by comparing three different e-learning interfaces in terms of usability aspects; effectiveness, user satisfaction and memorability. The aim is to find out which combination of the tested multimodal metaphors is the best one in terms of utilizing the auditory icons to supplement the presentation of learning material by virtual lecturer. The first experimental e-learning interface incorporates a speaking virtual lecturer with full body gestures along with supportive auditory icons. The second experimental e-learning interface includes the use of virtual lecturer speech in the absence of his body and accompanied with the same auditory icons used in the first interface. However, the third interface is similar to the second one in terms of using the virtual lecturer's speech but without any additional auditory icons. The obtained results have shown that the inclusion of auditory icons could enhance the usability and learning performance of e-learning interfaces much better if combined along with speaking virtual lectures in the absence of any body animation.

Keywords :

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Employing auditory icons to support virtual lecturers in e-learning interfaces

Authors : Marwan Alseid, Mohammad Azzeh, Yousef El Sheikh

Abstract : This paper presents a proposal to conduct an empirical study to compare three different e-learning interfaces in which three different ways of incorporating supportive auditory icons will be used. The main aim is to find out which is the best among these experimental interfaces in terms of usability aspects. The first experimental e-learning interface incorporates a speaking virtual lecturer with full body gestures along with supportive auditory icons. The second experimental e-learning interface includes the use of virtual lecturer speech in the absence of his body and accompanied with the same auditory icons used in the first interface.

Keywords :

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e‐Government in Jordan: challenges and opportunities

Authors : Yousef Elsheikh, Andrea Cullen, Dave Hobbs

Abstract : The purpose of this paper is to examine the challenges encountered in e‐government implementation, as well as the potential opportunities available in the context of Jordanian society.

Keywords :

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Towards e-government in the Middle East: A Jordanian Study

Authors : Yousef Elsheikh, Andrea Cullen, Dave Hobbs

Abstract : This paper attempts to reach an objective evaluation through reviewing the efforts and current achievements done by the government of Jordan to integrate emerging and existing modern information and communication technologies (ICTs) especially the Internet into its agencies. Examining briefly the challenges encountered in implementing E-Government initiatives in Jordan, as well as the potential opportunities experienced in the context of a Jordanian society. In addition, the understanding of the current status of E-Government in Jordan can help practitioners and researchers in the country to pursue development of the public sector organizations on one hand, and would be generally of importance for Jordan's economic future success on the other hand. However, since the development of an integrated E-Government infrastructure is a necessary issue for effectiveness and competency in the information age, the findings and implications of this review study reveal Jordan is still far behind some in utilizing ICTs for delivering government services and information on-line.

Keywords : E-Government, ICTs, Adoption and diffusion, Jordan, Middle East, Challenges and Opportunities

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A trust and reputation model for quality assessment of online content

Authors : Yousef Elsheikh

Abstract : This paper proposes a model to assess the trust and reputation of online content, relying on three factors namely user behavior, user reliability, and user tendency with homogeneous weights of interest to the user on the Internet. These homogenous weights will be used to measure the reputation of any online content. The proposed model has been validated and compared with some other well-known models, and showed a significant improvement in terms of the Mean Absolute Error (MAE). The proposed model is also good with sparse and dense datasets.

Keywords : Online content; Quality assessment; Trust and reputation model; User behaviour; User reliability; user tendency

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Analyzing the Role of Class Rebalancing Techniques in Software Defect Prediction

Authors :Y Alqasrawi, M Azzeh, Y Elsheikh

Abstract :Predicting software defects is an important task during software testing phase, especially for allocating appropriate resources and prioritizing testing tasks. Typically, classification algorithms are used to accomplish this task by using previously collected datasets. However, these datasets suffer from imbalanced label distribution where clean modules outnumber defective modules. Traditional classification algorithms cannot handle this nature in defect datasets because they assume the datasets are balanced. Failing to address this problem, the classification algorithm will produce a prediction biased towards the majority label. In the literature, there are several techniques designed to address this problem and most of them focus on data re-balancing. Recently, ensemble class imbalance techniques have emerged as an opposing approach to data rebalancing approaches. Regarding the software defect prediction, there are no studies examining the performance of ensemble class imbalance learning against data re-balancing approaches. This paper investigates the efficiency of ensemble class imbalance learning for software defect prediction. We conducted a comprehensive experiment that involved 12 datasets, six classifiers, nine class imbalance techniques, and 10 evaluation metrics. The experiments showed that ensemble approaches, particularly the Under Bagging technique, outperform traditional data re-balancing approaches, particularly when dealing with datasets that have high defect ratios.

Keywords :Software defect prediction, class imbalance, machine learning

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A soft computing approach for software defect density prediction

Authors :M Azzeh, Y Alqasrawi, Y Elsheikh

Abstract :Defect density is an essential software testing and maintenance aspect that determines the quality of software products. It is used as a management factor to distribute limited human resources successfully. The availability of public defect datasets facilitates building defect density prediction models using established static code metrics. Since the data gathered for software modules are often subject to uncertainty, it becomes difficult to deliver accurate and reliable predictions. To alleviate this issue, we propose a new prediction model that integrates gray system theory and fuzzy logic to handle the imprecision in software measurement. We propose a new similarity measure that combines the benefits of fuzzy logic and gray relational analysis. The proposed model was validated against defect density prediction models using public defect datasets. The defect density variable is frequently sparse because of the vast number of none-defected modules in the datasets. Therefore, we also check our proposed model's performance against the sparsity level. The findings reveal that the developed model surpasses other defect density prediction models over the datasets with high and very high sparsity ratios. The ensemble learning techniques are competitive choices to the proposed model when the sparsity ratio is relatively small. On the other hand, the statistical regression models were the most inadequate methods for such problems and datasets. Finally, the proposed model was evaluated against different degrees of uncertainty using a sensitivity analysis procedure. The results showed that our model behaves stably under different degrees of uncertainty.

Keywords :Software defect prediction, gray system theory , fuzzy logic, uncertainty, machine learning

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Examining the performance of kernel methods for software defect prediction based on support vector machine

Authors :Mohammad Azzeh, Yousef Elsheikh, Ali Bou Nassif, Lefteris Angelis

Abstract :Support Vector Machine (SVM) has been widely used to build software defect prediction models. Prior studies compared the accuracy of SVM to other machine learning algorithms but arrives at contradictory conclusions due to the use of different choices of kernel functions and metrics. Such a contradictory conclusion raises an important question about the performance of kernel functions, across different experimental conditions. To this end, the present study examines the impact and stability of four kernel functions with feature selection on the performance of SVM for software defect prediction. Strictly speaking, we examine the performance of nonlinear kernel functions against linear kernel function based on different experimental parameters such as data granularity, imbalance ratio of the dataset, and feature subsets. A large-scale study has been conducted using four kernel functions, ten feature subset selection thresholds based on the Information gain algorithm, 38 public datasets and one evaluation measure. This has resulted in 1520 experiments. The findings demonstrate that: 1) Not all nonlinear kernel functions significantly outperform linear, only RBF surpasses linear and other nonlinear kernel functions. 2) We don't have significant difference between kernel functions w.r.t. data granularity, we only found significant difference between RBF and other kernel function based on ‘function’ data granularity. 3) we also found that RBF can work significantly better than linear and other nonlinear function over datasets with very high and high imbalance ratios. 4) The performances of all kernel functions fluctuate over different feature subsets; However, using top 40% of the features would work best with all kernel functions. To conclude, we can recommend using SVM with RBF kernel for defects datasets because the performance of other kernel functions is limited.

Keywords :Software defect prediction, kernel methods, Support vector machine, machine learning

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An Optimized Case-Based Software Project Effort Estimation Using Genetic Algorithm

Authors :Shaima Hameed, Yousef Elsheikh, Mohammed Azzeh

Abstract :Software development companies have long suffered from inaccurate estimation of their software projects. This in turn led to huge losses, especially in the financial resources available for the project as well as the time required to complete it. As a result of this, the research community has developed different methods for estimating effort in software projects in the hope of achieving high levels of accuracy and efficiency in the use of available resources. Among those methods that have proven to be accurate in estimating the effort of software projects is the use of machine learning (ML) techniques, especially the case-based reasoning technique (CBR). This technique is based on adapting previously successful solutions for similar software projects. However, the CBR technique suffers from a problem which is its multiple parameters that are difficult to be tuned. This justifies the importance of the adaptation and adjustment process as an essential part of CBR to produce accurate and efficient results with least absolute estimation error. In this paper, one of the most efficient multi-objective evolutionary techniques, the Genetic Algorithm (GA), are used to help find the best set of classical CBR parameters (feature selection, feature weighting, similarity measures, and k number of nearest neighbors) to produce the most accurate effort estimates for software projects. The proposed CBR-GA model showed the effectiveness of using the GA algorithm to search for the best combination of CBR parameters and thus improve its accuracy. This in turn is beneficial for project managers in the early financial planning phase for effort estimation and thus project cost control. To validate the proposed CBR-GA model, we used a set of public benchmark datasets available on PROMISE data repository, in addition we used a set of reliable evaluation metrics. The obtained results are promising in terms of accuracy and significance tests. This implies the importance of search-based techniques for tuning effort estimation methods.

Keywords :Software cost estimation, case based reasoning (CBR), machine learning, genetic algorithm

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A New Learning to Rank Approach for Software Defect Prediction

Authors :Al-omari, Sara, Yousef Elsheikh, and Mohammed Azzeh

Abstract :Software defect prediction is one of the most active research fields in software development. The outcome of defect prediction models provides a list of the most likely defect-prone modules that need a huge effort from quality assurance teams. It can also help project managers to effectively allocate limited resources to validating software products and invest more effort in defect-prone modules. As the size of software projects grows, error prediction models can play an important role in assisting developers and shortening the time it takes to create more reliable software products by ranking software modules based on their defects. Therefore, there is need a learning-to-rank approach that can prioritize and rank defective modules to reduce testing effort, cost, and time. In this paper, a new learning to rank approach was developed to help the QA team rank the most defect-prone modules using different regression models. The proposed approach was evaluated on a set of standardized datasets using well-known evaluation measures such as Fault-Percentile Average (FPA), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Cumulative Lift Chart (CLC). Also, our proposed approach was compared with some other regression models that are used for software defect prediction, such as Random Forest (RF), Logistic Regression (LR), Support Vector Regression (SVR), Zero Inflated Regression (ZIR), Zero Inflated Poisson (ZIP), and Negative Polynomial Regression (NPR). Based on the results, the measurement criteria were different than each other as there was a gap in the accuracy obtained for defects prediction due to the nature of the random data, and thus was higher for RF and SVR, as well as FPA achieved better results than MAE and RMSE in this research paper.

Keywords :Software defect prediction, Learning to rank approach

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Understanding the Role of Multimodal Metaphors in E-Government Implementation: An Empirical Investigation

Authors :Mohammad Shkoukani, Yousef Elsheikh, and Hesham Abusaimeh

Abstract :Multimodal interaction metaphors have become a way to improve the usability of interactive systems, including e-government platform. This paper presents the experimental results of the inclusion of multimodal metaphors in e-government interfaces in order to enhance usability on the one hand, and trust between users on the other hand to eventually lead to the adoption and use of such interfaces on a large scale in the context under investigation. In this paper, multimodal metaphors that were used: visual and auditory stimuli and avatars. These investigations were evaluated by 40 users as it consisted of two different interfaces in each experiment to demonstrate the role of multimodal metaphors in the successful implementation of e-government interface platform. The results show the positive role of multimodal metaphors in increasing the level of usability in such interactive platforms on the one hand, and on the other hand increase the users trust, and learnability while interacting with those e-government platforms. Also, the results show the validity of the research approach adopted and the way in which users interact with e-government interface platform effectively and efficiently.

Keywords :E-government service, multimodal metaphor, Jordan

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What Facilitates the Delivery of Citizen-Centric E-Government Services in Developing Countries: Model Development and Validation Through Structural Equation Modeling

Authors :Yousef Elsheikh, Mohammad Azzeh

Abstract :Yet, existing research on the delivery of citizen-centric e-government services in developing countries is still lacking explanatory power for the following reasons: 1) focus either on the supply-side of these services, or on the demand-side separately, thus there is no enough research on the integration between them, and 2) focus on the results of previous research, thus ignoring the development of theories fit the new context under investigation by understanding the relationship between the implementation of ICT and social structures in the same context. This study aims to fill these gaps by employing a holistic approach to enable in-depth understanding and gain valuable insights for success factors in the delivery of citizencentric e-government services from multiple perspectives, and in the real context of one of the Arab countries, namely Jordan. This would reduce the gap between strategies and government policies on the one hand, and the perceptions of citizens on the other hand about the determinants of the delivery of citizen-centric e-government services in developing countries, allowing a better understanding of citizens' needs and priorities that must be taken into account by those governments in order to ensure the success of those services on a large scale. This study is part of a two-phase research aims to propose an integrated model of success factors in the delivery of citizen centric e-government services and then validate it in the context of developing countries in general and Jordan in particular. The first phase, which is beyond the scope of this study, employs grounded theory method to develop a research model, as well as survey instruments necessary to validate it. The second phase, which is within the scope of this study, employs survey questionnaire method to validate the research model by using multivariate and structural equation modeling (SEM). The results show that factors such as the perception of ease of use as well as the perception of usefulness are no longer affect the delivery of citizen-centric e-government services in this particular context. The results also show that the perception of organizational loyalty through more involvement of citizens in the decision-making process of change, as well as awareness-raising campaigns for such e-services, is among the positive and significant factors that affect the delivery of citizen-centric e-government services in the same context. Perception of trust, quality and security in e-services, all of these have significance and positive impact on the delivery of such e-services in this particular context. Website design is also among the factors that have significance and a positive impact in the same context. Most importantly, the results show that the national e-readiness, including the macro political environment, legal and regulatory environment, socio-economic environment and infrastructure related to ICT, is among the factors that have significance, but did not reach to become positive in this particular context. However, the modern Arab culture stands out as among the factors that have significance, as well as a positive impact in this context. Finally, this study concludes by highlighting the theoretical and practical implications, limitations and future directions.