Abstract: An enhanced e-learning network based on integration between cloud environment and Web is probably suggested for the existing and upcoming e-learning tools that are considered as Software as a Service (SaaS). This paper examines three most recent patents that were awarded between February 2012 and May 2014 in the area of e-learning. In addition, we have proposed a new direction which is the possibility of integration of the E-learning processes with Cloud Processes. This kind of integration needs to enable the Software Engineering concepts. Thus it is possible to design and develop the E-learning processes and then the Cloud Computing processes are integrated with the e-learning application process to formulate a new platform is called a Cloud E-learning system/platform. It is expected that such integration is an iterative, incremental development process leads to faster delivery of more constructive software. To demonstrate the proposed approach, a proposed deployment of Cloud e-learning software has been presented.

Software program testing is the procedure of exercising a software component with a selected set of test cases as a way to discover defects and assess quality. Using software testing automation, especially the generating of testing data increases the effectiveness and efficiency of software testing as a whole. Instead of creating testing data from scratch, Big Data (BD) offers an important source of testing data. Although it is a good source, there is a need to select a proper set of testing data for the sake of selecting an optimal sub-domain input values from the BD. To refine the efficiency of software testing, this paper proposes a hybrid Genetic Algorithm and Binary Search (BSGA) technique that is used for detecting the error-prone path in a program. The BSGA combines the Genetic Algorithm (GA) with the Binary Search (BS) algorithm that uses the BD as input values for the program path coverage, and thus enhances the software testing. The BSGA represents a robust nonlinear search technique and a better quality solution, which therefore results in a cost reduction in the software testing industry. The experiments show that the results approved the impact of using the BS to enhance the performance of the GA, in terms of finding optimal test cases and test data for the input Big Data domain values. Whereas, these results minimize the cost of testing.

Although current computer-aided software engineering tools support developers in composing a program, there is no doubt that more flexible supportive tools are needed to address the increases in the complexity of programs. This need can be met by automating the intellectual activities that are carried out by humans when composing a program. This paper aims to automate the composition of a programming language code from pseudocode, which is viewed here as a translation process for a natural language text, as pseudocode is a formatted text in natural English language. Based on this view, a new automatic code generator is developed that can convert pseudocode to C# programming language code. This new automatic code generator (ACG), which is called CodeComposer, uses natural language processing (NLP) techniques such as verb classification, thematic roles, and semantic role labeling (SRL) to analyze the pseudocode. The resulting analysis of linguistic information from these techniques is used by a semantic rule-based mapping machine to perform the composition process. CodeComposer can be viewed as an intelligent computer-aided software engineering (I_CASE) tool. An evaluation of the accuracy of CodeComposer using a binomial technique shows that it has a precision of 88%, a recall of 91%, and an F-measure of 89%.

This paper aims to demonstrate the development of an expert code generator using rule-based and frames knowledge representation techniques (ECG-RF). The ECG-RF system presented in this paper is a passive code generator that carries out the task of automatic code generation in fixed-structure software. To develop an ECG-RF system, the artificial intelligence (AI) of rule-based system and frames knowledge representation techniques was applied to a code generation task. ECG-RF fills a predefined frame of a certain fixed-structure program with code chunks retrieved from ECG-RF's knowledge base. The filling operation is achieved by ECG-RF's inference engine and is guided by the information collected from the user via a graphic user interface (GUI). In this paper, an ECG-RF system for generating a device driver program is presented and implemented with VBasic software. The results show that the ECG-RF design concept is reasonably reliable.

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