CLC number: TP311.5
On-line Access: 2021-07-20
Received: 2020-03-26
Revision Accepted: 2020-06-23
Crosschecked: 2021-06-08
Cited: 0
Clicked: 5088
Citations: Bibtex RefMan EndNote GB/T7714
Haijuan Wang, Guohua Shen, Zhiqiu Huang, Yaoshen Yu, Kai Chen. Analyzing close relations between target artifacts for improving IR-based requirement traceability recovery[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2000126 @article{title="Analyzing close relations between target artifacts for improving IR-based requirement traceability recovery", %0 Journal Article TY - JOUR
通过分析目标制品间的紧密关系改进基于信息检索的需求追踪恢复1南京航空航天大学计算机科学与技术学院,中国南京市,211106 2软件新技术与产业化协同创新中心,中国南京市,211106 3高安全系统的软件开发与验证技术工信部重点实验室,中国南京市,211106 摘要:需求追踪是一项重要且昂贵的任务,它创建了从需求到不同软件制品的追踪链。这些追踪链可以帮助工程师节约软件维护时间并降低维护复杂性。信息检索技术在需求追踪中应用广泛。它使用软件制品之间的文本相似性来创建链接。然而,如果两个制品不共享或仅共享少量单词,信息检索性能可能非常差。已有一些方法通过考虑目标制品之间的关系来增强信息检索,但它们仅限于代码,而无法应用于其他类型的目标制品。为克服这一局限,本文提出一种将信息检索方法与目标制品间的紧密关系相结合的自动化方法。具体地,我们增加了对目标制品间紧密关系的考虑,而不仅仅是从需求到目标制品的文本匹配。此外,在考虑目标制品间的关系时,该方法并不局限于目标制品的类型。我们在5个公共数据集上进行了实验,并考虑了需求和不同类型的软件制品之间的追踪链。结果表明,在相同的查全率下,5个数据集的查准率较之基线方法分别提高40%、8%、20%、4%和6%。5个数据集的查准率平均提高15.6%,这表明在相同条件下,本文所提方法优于基线方法。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
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