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首页> 外文期刊>IEEE Transactions on Software Engineering >Assessing Software Service Quality and Trustworthiness at Selection Time
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Assessing Software Service Quality and Trustworthiness at Selection Time

机译：在选择时评估软件服务质量和可信度

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摘要

The integration of external software in project development is challenging and risky, notably because the execution quality of the software and the trustworthiness of the software provider may be unknown at integration time. This is a timely problem and of increasing importance with the advent of the SaaS model of service delivery. Therefore, in choosing the SaaS service to utilize, project managers must identify and evaluate the level of risk associated with each candidate. Trust is commonly assessed through reputation systems; however, existing systems rely on ratings provided by consumers. This raises numerous issues involving the subjectivity and unfairness of the service ratings. This paper describes a framework for reputation-aware software service selection and rating. A selection algorithm is devised for service recommendation, providing SaaS consumers with the best possible choices based on quality, cost, and trust. An automated rating model, based on the expectancy-disconfirmation theory from market science, is also defined to overcome feedback subjectivity issues. The proposed rating and selection models are validated through simulations, demonstrating that the system can effectively capture service behavior and recommend the best possible choices.
机译：在项目开发中集成外部软件具有挑战性和风险，特别是因为在集成时，软件的执行质量和软件提供商的可信度可能是未知的。这是一个及时的问题，随着SaaS服务交付模式的出现，它变得越来越重要。因此，在选择要使用的 SaaS 服务时，项目经理必须识别和评估与每个候选人相关的风险水平。信任通常通过声誉系统进行评估;但是，现有系统依赖于消费者提供的评级。这引发了许多问题，涉及服务评级的主观性和不公平性。本文介绍了一个用于声誉感知软件服务选择和评级的框架。为服务推荐设计了选择算法，根据质量、成本和信任度为 SaaS 消费者提供最佳选择。还定义了一个基于市场科学的期望-不确认理论的自动评级模型，以克服反馈主观性问题。通过仿真验证了所提出的评级和选择模型，表明该系统能够有效地捕捉服务行为并推荐最佳选择。

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