可追溯性挑战2013:可追溯性实验的统计分析:肯塔基大学软件验证和验证研究实验室(SVVRL

Mark Hays, J. Hayes, A. Stromberg, A. Bathke
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引用次数: 6

摘要

可追溯性实验的一个重要方面是比较技术的能力。为了保证适当的比较,有必要对技术应用中收集的因变量进行统计分析。目前,TraceLab中缺乏支持这种分析的组件。软件验证和验证研究实验室(SVVRL)和肯塔基大学的统计部门已经开发了这些组件的集合,以及用于确定应用哪种类型的分析(参数化的,非参数化的)的工作流。组件使用业界认可的R算法。这些组件已经使用独立的标准统计算法对公开可用的数据集进行了验证。这项工作解决了有目的的大挑战(研究项目4)和具有成本效益的大挑战(研究项目4)以及有价值的大挑战(研究项目6)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Traceability Challenge 2013: Statistical analysis for traceability experiments: Software verification and validation research laboratory (SVVRL) of the University of Kentucky
An important aspect of traceability experiments is the ability to compare techniques. In order to assure proper comparison, it is necessary to perform statistical analysis of the dependent variables collected from technique application. Currently, there is a lack of components in TraceLab to support such analysis. The Software Verification and Validation Research Laboratory (SVVRL) and the Statistics Department of the University of Kentucky have developed a collection of such components as well as a workflow for determining what type of analysis to apply (parametric, non-parametric). The components use industry-accepted R algorithms. The components have been validated using independent standard statistical algorithms applied to publicly available datasets. This work addresses the Purposed grand challenge (research project 4) and Cost-Effective Grand Challenge (research project 4) as well as the Valued Grand Challenge - research project 6.
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