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Software Metrics Artifacts Making Web Quality Measurable

机译:软件指标工件使网络质量可测量

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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:采矿开源储存库介绍了一种有效的方法,以便在各种技术中实施实践经验软件工程。内核开发(Linux)首先是Internet(Chromium)和更多最近的云编排(Kubernetes)和机器学习(Tensorflow)是不仅适用于开源生态系统的基本作品,还为行业领先的软件创新。经验软件工程持续了解这些项目的更好理解,减少了更多的采用障碍。在这项工作中,我们专注于经验质量评估开发软件度量伪像,以使网络组件质量可测量。在审查最先进的软件测量的主要框架之后,我们将向我们的建议提供对网站组件,数据收集,测量和预测的质量指标的实证评估,讨论主要福利以及所选择方法的一些缺点瞄准未来的作品。

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