基于接近的可追溯性:使用排序检索和基于集合的度量的经验验证

Wei-Keat Kong, J. Hayes
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引用次数: 15

摘要

可追溯性链接的自动生成试图减少构建需求可追溯性矩阵(rtm)的负担,这些需求可追溯性矩阵将在验证和确认任务(如关键性评估或变更影响分析)中使用之前由人工分析师进行审查。信息检索(IR)技术,特别是向量空间模型(VSM),已经成功地用于构建文本工件可追溯性矩阵。VSM的一个限制是,它忽略了被跟踪的文本工件中的单词或术语位置以及单词之间的关系。本文提出了一种考虑术语位置的VSM增强方法,并使用排名检索和基于集的度量方法在四个数据集上对其进行验证。这两种类型的度量提供了两种可追溯性技术之间更详细的比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Proximity-based traceability: An empirical validation using ranked retrieval and set-based measures
The automatic generation of traceability links attempts to reduce the burden of building requirements traceability matrices (RTMs) that will be vetted by a human analyst before use in verification and validation tasks such as criticality assessment or change impact analysis. Information Retrieval (IR) techniques, notably the Vector Space Model (VSM), have been used with some success to build textual artifact traceability matrices. A limitation of the VSM is that it disregards word or term location and the relationship between words in the textual artifacts being traced. This paper presents a VSM enhancement with consideration for term location, validating it on four datasets using ranked retrieval and set-based measures. These two types of measures provide a more detailed comparison between the two traceability techniques.
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