Advancement in information system leads organizations to apply e-learning system to train their employees in order to enhance its performance. In this respect, applying web based training will enable the organization to train their employees quickly, efficiently and effectively anywhere at any time. This research aims to extend Unified Theory of Acceptance and Use Technology (UTAUT) using some factors such flexibility of web based training system, system interactivity and system enjoyment, in order to explain the employees' intention to use web based training system. A total of 290 employees have participated in this study. The findings of the study revealed that performance expectancy, facilitating conditions, social influence and system flexibility have direct effect on the employees' intention to use web based training system, while effort expectancy, system enjoyment and system interactivity have indirect effect on employees' intention to use the system. KEYWORDSUTAUT, structural equation modeling, system enjoyment, system flexibility and system interactivity

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During the software development life cycle, testing is highly needed to assure the quality of the software process and product. For many years, researchers and practitioners came-up with a variety of software tools to automate the testing process.In this paper, we will classify and distribute a set of testing tools over the types of testing (testing methods) for three types of software products (web application, application software, and network protocol). However, the outcomes of this classification are dedicated to the researchers and practitioners in software testing to know which types of testing have no or limited automated tools.

The various model that has been used to predict, datamining, and information retrieval are useful to use through the traditional database, due to big data the prediction should derive in a different role that conduct the hidden structure data based on a stability scale to allow discovering accrue unsupervised drug data. Especially, the drug data must be understandable to analysts. Following this approach, conduct the stability drug data through computation methods are quality measurements, preprocess data, k-mean cluster, and decision tree. This approach seeks to identify the data by two dimensions (vertically and horizontally), which extrapolations, compilation, and interpretation values of the dataset while considering individual attributes. A comparison with clusters defines the set for features using balance value by K-mean algorithm to determine the k clusters that consider the set of features based on two values 0 and 1, which given the discernible between dependent and independent class target, and pinpoint the relationship among them. Keywords: Big Data, Discretize, k-mean cluster Stability, Target drug

Requirement gathering is a vital step in software engineering. Even though many recent researches concentrated on the improvement of the requirement gathering process, many of their works lack completeness especially when the number of users is large. Data Mining techniques have been recently employed in various domains with promising results. In this work, we propose an intelligent recommender system for requirement engineering based on association rule analysis, which is a main category in Data Mining. Such recommender would contribute in enhancing the accuracy of the gathered requirements and provide more comprehensive results. Conducted experiments in this work prove that FP Growth outperformed Apriori in terms of execution and space consumption, while both methods were efficient in term of accuracy.