可追溯性挑战2013:查询+语义跟踪增强(QuEST):肯塔基大学软件验证和验证研究实验室(SVVRL)

Wenbin Li, J. Hayes
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引用次数: 3

摘要

我们提出了在解决无所不在的大挑战中进行可追溯性挑战的过程和方法,研究项目3。查询中包含的术语(以及文档集合术语,因此使用“+”)已得到增强,以包含语义标记,这些标记指示术语是代表操作还是代表代理。这些信息是通过调用Senna语义角色标注工具获得的。然后使用TraceLab中的标准TF-IDF组件恢复跟踪链接。QuEST方法应用于四个数据集。基于提供的答案集的结果表明,当工件使用自然语言时,QuEST提高了两个数据集的两个工件对的平均平均精度(MAP),但通常不优于未使用自然语言的非增强数据集。我们就这一发现提供一些见解。
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: Query+ enhancement for semantic tracing (QuEST): Software verification and validation research laboratory (SVVRL) of the University of Kentucky
We present the process and methods applied in undertaking the Traceability Challenge in addressing the Ubiquitous Grand Challenge, Research Project 3. Terms contained within queries (along with document collection terms, hence the “+”) have been enhanced to include semantic tags that indicate whether a term represents an action or an agent. This information is obtained by calling the Senna semantic role labeling tool. The standard TF-IDF component in TraceLab is then used to recover trace links. The QuEST method was applied to four datasets. Results based on the provided answer sets show that QuEST improved Mean Average Precision (MAP) for two artifact pairs of two of the datasets when the artifacts used natural language, but generally did not outperform unaugmented datasets not using natural language. We provide insights on this finding.
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