Abstract:The e-learning is a future education that would substitute to the traditional learning, which overcomes the spatial and temporal limitations. The e-learning field growth rapidly in the recently years due to its importance in the private and public sectors. This paper focuses on the system quality of e-learning. The e-learning system depends on the quality to be successful and the real success is sustained usage. The users of e-learning system will stop using such system if the quality is poor, where often the users reject the system unless they try it, where the intentions of continuing using the system are still weak. There are several attributes and functionalities that can have an impact on the use of e-learning based on user perspective, such as are usability, reliability and efficiency. These quality attributes are used to reflect the quality of the software product. The intended objective of this study is to develop an appropriate model for e-learning to satisfy the users from the side of using the e-learning system, where carried the discussion of twenty-four model with thirty attributes. Finally, the result of this study adopted the process of structural equation model which indicated that the hypotheses have positive relations.
Abstract-Despite the importance of web accessibility in recent years, websites remain partially or completely inaccessible to certain sectors of the population. This is due to several reasons, including web developers' little or no experience in accessibility and the lack of accurate information about the best ways to quickly and easily identify accessibility problems using different Accessibility Evaluation Methods (AEMs). This paper surveys accessibility literature and presents a general overview of the primary challenges of accessibility barriers on websites. In this sense, we critically investigate main challenges forms related to accessibility including standards and guidelines (WCAG 2.0), during website's design and development and during evaluation. Finally, a set of recommendations such as enforcing accessibility legislations are presented to overcome some challenges.
Abstract. Formal methods can in principle provide the highest levels of assurance of code safety by providing formal proofs as explicit evidence for the assurance claims. However, the proofs are often complex and difficult to relate to the code, in particular if it has been generated automatically. They may also be based on assumptions and reasoning principles that are not justified. This causes concerns about the trustworthiness of the proofs and thus the assurance claims. Here we present an approach to systematically construct safety cases from information collected during a formal verification of the code, in particular from the construction of the logical annotations necessary for a formal, Hoare-style safety certification. Our approach combines a generic argument that is instantiated with respect to the certified safety property (i.e., safety claims) with a detailed, program-specific argument that can be derived systematically because its structure directly follows the course the annotation construction takes through the code. The resulting safety cases make explicit the formal and informal reasoning principles, and reveal the top-level assumptions and external dependencies that must be taken into account. However, the evidence still comes from the formal safety proofs. Our approach is independent of the given safety property and program, and consequently also independent of the underlying code generator. Here, we illustrate it for the AutoFilter system developed at NASA Ames.
Formal proofs provide detailed justification for the validity of claims and are widely used in formal software development methods. However, they are often complex and difficult to understand, because the formalism in which they are constructed and encoded is usually machine-oriented, and they may also be based on assumptions that are not justified. This causes concerns about the trustworthiness of using formal proofs as arguments in safety-critical applications. Here, we present an approach to develop safety cases that correspond to formal proofs found by automated theorem provers and reveal the underlying argumentation structure and top-level assumptions. We concentrate on natural deduction style proofs, which are closer to human reasoning than resolution proofs, and show how to construct the safety cases by covering the natural deduction proof tree with corresponding safety case fragments. We also abstract away logical book-keeping steps, which reduces the size of the constructed safety cases. We show how the approach can be applied to the proofs found by the Muscadet prover.
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