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Software Metrics Artifacts Making Web Quality Measurable

机译:可衡量Web质量的软件度量工件

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Mining open source repositories introduces an effective approach to put in practice empirical software engineering in a variety of technologies. Kernel development (Linux) first and then Internet (Chromium) and more recently cloud orchestration (Kubernetes) and machine learning (TensorFlow) are fundamental pieces not just for open source ecosystem but also for the industry leading software innovation. Empirical software engineering sustains a better understanding of these projects, reducing even more the barriers for adoption. In this work we focus on empirical quality assessment developing software metrics artifacts to make web components quality measurable. After reviewing the state of the art and main frameworks for software measurement, we will present our proposal for the empirical evaluation of quality metrics for web components, data collection, measurement and prediction, discussing main benefits and some drawback of the selected approach, which will be aimed at future works.
机译:挖掘开源资源库引入了一种有效的方法,可将各种技术中的经验软件工程付诸实践。首先是内核开发(Linux),然后是Internet(Chromium),最近是云编排(Kubernetes)和机器学习(TensorFlow),它们不仅是开源生态系统的基础,而且还是行业领先的软件创新的基础。经验软件工程可以更好地理解这些项目,从而减少采用的障碍。在这项工作中,我们专注于经验质量评估,开发软件指标工件以使Web组件质量可测量。在回顾了软件测量的最新技术和主要框架之后,我们将提出有关对Web组件质量指标,数据收集,测量和预测进行经验评估的提案,并讨论所选方法的主要优点和缺点。针对未来的作品。

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