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: 5174
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, 2021, 22(7): 957-968.
@article{title="Analyzing close relations between target artifacts for improving IR-based requirement traceability recovery",
author="Haijuan Wang, Guohua Shen, Zhiqiu Huang, Yaoshen Yu, Kai Chen",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="22",
number="7",
pages="957-968",
year="2021",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2000126"
}
%0 Journal Article
%T Analyzing close relations between target artifacts for improving IR-based requirement traceability recovery
%A Haijuan Wang
%A Guohua Shen
%A Zhiqiu Huang
%A Yaoshen Yu
%A Kai Chen
%J Frontiers of Information Technology & Electronic Engineering
%V 22
%N 7
%P 957-968
%@ 2095-9184
%D 2021
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000126
TY - JOUR
T1 - Analyzing close relations between target artifacts for improving IR-based requirement traceability recovery
A1 - Haijuan Wang
A1 - Guohua Shen
A1 - Zhiqiu Huang
A1 - Yaoshen Yu
A1 - Kai Chen
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 22
IS - 7
SP - 957
EP - 968
%@ 2095-9184
Y1 - 2021
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2000126
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.
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