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An in-depth study of the effects of methods on the dataset selection of public development projects

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成果类型:
期刊论文
作者:
Cheng, Can;Li, Bing*;Li, Zengyang;Liang, Peng;Yang, Xu
通讯作者:
Li, Bing
作者机构:
[Cheng, Can; Liang, Peng; Li, Bing] Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.
[Li, Zengyang] Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.
[Yang, Xu] Huawei Technol, Nanjing, Peoples R China.
通讯机构:
[Li, Bing] W
Wuhan Univ, Sch Comp Sci, Wuhan, Peoples R China.
语种:
英文
关键词:
data mining;software engineering
期刊:
IET Software
ISSN:
1751-8806
年:
2022
卷:
16
期:
2
页码:
146-166
基金类别:
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61832014]; Natural Science Foundation of Hubei Province of ChinaNatural Science Foundation of Hubei Province [2021CFB578]
机构署名:
本校为其他机构
院系归属:
计算机学院
摘要:
Public development projects (PDPs) and documented public development projects (DPDPs) are two types of projects that can provide valuable information on how developers and users participate in OSS projects. However, it is hard for researchers to effectively select PDPs and DPDPs due to the lack of specific project selection methods for these two types of projects. To address this problem, a standard dataset was labelled and the base line methods (i.e. selecting projects according to a single feature like star number) under 60 configurations and...

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