Abstract The concept of development effort generally means the time or the cost of developing a software service. An essential factor to successfully manage and control a project is the accurate estimation of the development effort and an over and underestimation lead to the loss of project resources. So far, different effort estimation models have been presented in three domains: expert judgment, algorithmic methods,... The concept of development effort generally means the time or the cost of developing a software service. An essential factor to successfully manage and control a project is the accurate estimation of the development effort and an over and underestimation lead to the loss of project resources. So far, different effort estimation models have been presented in three domains: expert judgment, algorithmic methods, and machine learning methods. Recently, several approaches in the last domain, machine learning, have been applied for software service development effort, which had a higher performance in comparison to the other two domains. This paper presents an empirical evaluation of the performance and accuracy of five main machine learning methods using the correlation analysis approach and investigates the effects of feature selection on the estimation accuracy. The evaluations and comparisons are performed using two well-known and real-world datasets, NASA and ISBSG, and two artificial datasets, Moderate and Severe. Finally, the obtained results provide a clear illustration of the performance of these machine learning methods and the effects of feature selection on the estimation accuracy. Read More Read Less

Abstract Cloud computing is a phenomenon of last few years on the technology field. This phenomenon has a positive effect on the education process which represents cloud-based e-learning system. However, the new technologies require collect, share and exchange a large amount of information frequently without users even being aware of it. The main objective of this paper is to develop universal Cloud Based E-Learning... Cloud computing is a phenomenon of last few years on the technology field. This phenomenon has a positive effect on the education process which represents cloud-based e-learning system. However, the new technologies require collect, share and exchange a large amount of information frequently without users even being aware of it. The main objective of this paper is to develop universal Cloud Based E-Learning Users’ Information Privacy Concerns Instrument. The development involves of selecting the right elements that are assumed to determine the privacy concerns and examining the impact of every element in measurement. The instrument is divided into 5 parts, 16 factors and total of 61 items. The results of this study showed that first part of the instrument that examined trust beliefs and risk belief has high internal consistency and satisfactory factor loadings. The second part of the instrument, which contained 4 factors, and total 11 items that examined concern for information privacy (CFIP), all 3 factors had high internal consistency and satisfactory factor loadings. The third part of the instrument, which contained 5 items that examined the Global Information Privacy Concern (GIPC), all 5 items is significantly disrupted internal consistency. The fourth part of the instrument consisted of 3 factors and total 10 items that measured User Information Privacy Concern (IUIPC), had high internal consistency and satisfactory factor loadings. The fifth part of instrument, which examined 7 factors and total 25 items that measured that Cloud Computing privacy Concerns (CCPC), had high internal consistency and the privacy breaches factor should consist of 3 items (one item had low factor loading). Even though developing instrument needs more effort and validation, initial results illustrated that this instrument would be an excellent and reliable to evaluate cloud-based e-learning users’ information privacy concerns. Read More Read Less