Open AccessAn in‐depth study of the effects of methods on the dataset selection of public development projects
Author(s) -
Cheng Can,
Li Bing,
Li Zengyang,
Liang Peng,
Yang Xu
Publication year - 2022
Publication title -
iet software
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.305
H-Index - 43
eISSN - 1751-8814
pISSN - 1751-8806
DOI - 10.1049/sfw2.12050
Subject(s) - measure (data warehouse) , selection (genetic algorithm) , computer science , base (topology) , feature selection , feature (linguistics) , machine learning , artificial intelligence , data mining , mathematics , mathematical analysis , linguistics , philosophy
Abstract 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 the machine learning methods under 18 configurations were tested to identify the best configurations in precision and F‐measure for selecting PDPs and DPDPs. The results show that (1) to select PDPs or DPDPs with a high precision, the base line method is the best with precision of 0.877 (PDPs) and 0.831 (DPDPs); (2) to select PDPs or DPDPs with a high F‐measure, the machine learning methods are the best, with F‐measure of 0.817 (PDPs) and 0.789 (DPDPs); (3) existing sample selection strategies can be combined with the machine learning methods, and the precision of selecting PDPs can be increased by 6.39%–41.33% and the precision of selecting DPDPs can be can be increased by 35.50%–269.02%.