What can we learn from quality assurance badges in open-source software?

被引:0
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作者
Feng Li
Yiling Lou
Xin Tan
Zhenpeng Chen
Jinhao Dong
Yang Li
Xuanzhi Wang
Dan Hao
Lu Zhang
机构
[1] Peking University,MoE Key Lab of HCST, School of Computer Science
[2] Purdue University,Department of Computer Science
[3] Beihang University (BUAA),School of Computer Science & Engineering
[4] University College London (UCL),Department of Computer Science
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关键词
quality assurance; badge; open-source software; code quality; empirical study;
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摘要
In the development of open-source software (OSS), many developers use badges to give an overview of the software and share some key features/metrics conveniently. Among various badges, quality assurance (QA) badges make up a large proportion and are the most prevalent because QA is of vital importance in software development, and ineffective QA may lead to anomalies or defects. In this paper, we focus on QA badges in open-source projects, which present quality assurance information directly and instantly, and aim to produce some interesting findings and provide practical implications. We collect and analyze 100000 projects written in popular programming languages from GitHub and conduct a comprehensive empirical study both inside and outside QA badges. Inside QA badges, we build a category classification for all QA badges based on the properties they focus on, which shows the types of QA badges developers use. Then, we analyze the frequency of the properties that QA badges focus on, and property combinations, too, which present their use status. We find that QA badges focus on various properties while developers give different preferences to different properties. The use status also differs between different programming languages. For example, projects written in C focus on Security to a great extent. Our findings also provide implications for developers and badge providers. Outside QA badges, we conduct a correlation analysis between QA badges and some software metrics that have potential relationships with code quality, contribution quality, and popularity. We find that QA badges have statistically significant correlations with various software metrics.
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