Full Text:   <1377>

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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: 2560

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Guo-hua Shen

https://orcid.org/0000-0003-2182-0019

Haijuan Wang

https://orcid.org/0000-0002-2088-3563

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Frontiers of Information Technology & Electronic Engineering  2021 Vol.22 No.7 P.957-968

http://doi.org/10.1631/FITEE.2000126


Analyzing close relations between target artifacts for improving IR-based requirement traceability recovery


Author(s):  Haijuan Wang, Guohua Shen, Zhiqiu Huang, Yaoshen Yu, Kai Chen

Affiliation(s):  College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; more

Corresponding email(s):   18895681787@163.com, ghshen@nuaa.edu.cn, zqhuang@nuaa.edu.cn, yaoshen.yu@outlook.com

Key Words:  Requirement traceability, Information retrieval, Close relations, Target artifacts


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, 2021, 22(7): 957-968.

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Abstract: 
requirement traceability is an important and costly task that creates trace links from requirements to different software artifacts. These trace links can help engineers reduce the time and complexity of software maintenance. The information retrieval (IR) technique has been widely used in requirement traceability. It uses the textual similarity between software artifacts to create links. However, if two artifacts do not share or share only a small number of words, the performance of the IR can be very poor. Some methods have been developed to enhance the IR by considering relations between target artifacts, but they have been limited to code rather than to other types of target artifacts. To overcome this limitation, we propose an automatic method that combines the IR method with the close relations between target artifacts. Specifically, we leverage close relations between target artifacts rather than just text matching from requirements to target artifacts. Moreover, the method is not limited to the type of target artifacts when considering the relations between target artifacts. We conduct experiments on five public datasets and take account of trace links between requirements and different types of software artifacts. Results show that under the same recall, the precisions on the five datasets improve by 40%, 8%, 20%, 4%, and 6%, respectively, compared with the baseline method. The precision on the five datasets improves by an average of 15.6%, showing that our method outperforms the baseline method when working under the same conditions.

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

汪海娟1,沈国华1,2,3,黄志球1,2,3,俞垚慎1,陈凯1
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:需求追踪;信息检索;紧密关系;目标制品

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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