Evaluating the practical use of different measurement scales in requirements prioritisation

L. Karlsson, Martin Höst, B. Regnell
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引用次数: 32

Abstract

The importance of prioritising requirements is widely recognised. A number of different techniques for prioritising requirements have been proposed, some based on an ordinal scale, others on a ratio scale. Some measurement scales provide more information than others, i.e. the ratio scale is richer than the ordinal scale. This paper aims to investigate the differences between the scales used in prioritisation. This is important since techniques using a richer scale tend to be more time-consuming and complex to use. Thus, there is a trade-off between simple techniques only providing ranks and complex techniques providing information about the relative distance between requirements priorities. The paper suggests an approach to measure the skewness of the ratio distribution and a way to use the cost-value approach on ordinal scale data. Four different empirical data sets were used to verify the suggested approaches. The skewness measure seems feasible to determine in which cases the ratio scale is valuable. It indicates that some of our subjects tend to use the extreme values of the scale while others are more modest. The cost-value approach based on ordinal scale data also seems feasible. The requirements selection decisions based on ordinal scale data agree substantially with the decisions based on ratio scale data.
评估需求优先级中不同度量尺度的实际使用
对需求进行优先排序的重要性已得到广泛认可。已经提出了许多不同的技术来确定需求的优先级,有些是基于顺序刻度,有些是基于比例刻度。有些测量量表比其他量表提供更多的信息,即比例量表比序数量表更丰富。本文旨在探讨在优先级使用的尺度之间的差异。这一点很重要,因为使用更丰富的尺度的技术往往更耗时,使用起来也更复杂。因此,在仅提供等级的简单技术和提供有关需求优先级之间的相对距离的复杂技术之间存在一种权衡。本文提出了一种衡量比例分布偏度的方法,以及一种在有序尺度数据上使用成本值法的方法。使用四种不同的经验数据集来验证建议的方法。偏度测量似乎是可行的,以确定在哪些情况下,比例尺度是有价值的。这表明我们的一些研究对象倾向于使用量表的极端值,而另一些则更温和。基于有序尺度数据的成本价值方法似乎也是可行的。基于有序尺度数据的需求选择决策与基于比例尺度数据的决策基本一致。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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