可追溯性
计算机科学
源代码
冗余(工程)
跟踪(心理语言学)
预处理器
注释
软件质量
编码(集合论)
数据挖掘
特征(语言学)
情报检索
软件工程
程序设计语言
人工智能
软件
软件开发
集合(抽象数据类型)
哲学
操作系统
语言学
作者
Bangchao Wang,Yang Deng,Ruiqi Luo,Huan Jin
标识
DOI:10.1109/qrs57517.2022.00110
摘要
In information retrieval-based (IR-based) requirements traceability research, a great deal of researches have focused on establishing trace links between requirements and source code. However, as the description styles of source code and requirements are very different, how to better preprocess the code is crucial for the quality of trace link generation. This paper aims to draw empirical conclusions about code feature extraction, annotation importance assessment, and annotation redundancy removal through comprehensive experiments, which impact the quality of trace links generated by IR-based methods between requirements and source code. The results show that when the average annotaion density is higher than 0.2, feature extraction is recommended. Removing redundancy from code with high annotation redundancy can enhance the quality of trace links. The above experiences can help developers to improve the quality of trace link generation and provide them with advice on writing code.
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