Simon Koch, David Klein, and Martin Johns (TU Braunschweig)

Are GitHub stars a good surrogate metric to assess the importance of open-source code? While security research frequently uses them as a proxy for importance, the reliability of this relationship has not been studied yet. Furthermore, its relationship to download numbers provided by code registries – another commonly used metric – has yet to be ascertained. We address this research gap by analyzing the correlation between both GitHub stars and download numbers as well as their correlation with detected deployments across websites. Our data set consists of 925 978 data points across three web programming languages: PHP, Ruby, and JavaScript. We assess deployment across websites using 58 hand-crafted fingerprints for JavaScript libraries. Our results reveal a weak relationship between GitHub Stars and download numbers ranging from a correlation of 0.47 for PHP down to 0.14 for JavaScript, as well as a high amount of low star and high download projects for PHP and Ruby and an opposite pattern for JavaScript with a noticeably higher count of high star and apparently low download libraries. Concerning the relationship for detected deployments, we discovered a correlation of 0.61 and 0.63 with stars and downloads, respectively. Our results indicate that both downloads and stars pose a moderately strong indicator of the importance of client-side deployed JavaScript libraries.

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Linkai Zheng (Tsinghua University), Xiang Li (Tsinghua University), Chuhan Wang (Tsinghua University), Run Guo (Tsinghua University), Haixin Duan (Tsinghua University; Quancheng Laboratory), Jianjun Chen (Tsinghua University; Zhongguancun Laboratory), Chao Zhang (Tsinghua University; Zhongguancun Laboratory), Kaiwen Shen (Tsinghua University)

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Make your IoT environments robust against adversarial machine learning...

Hamed Haddadpajouh (University of Guelph), Ali Dehghantanha (University of Guelph)

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Towards Anonymous Chatbots with (Un)Trustworthy Browser Proxies

Dzung Pham, Jade Sheffey, Chau Minh Pham, and Amir Houmansadr (University of Massachusetts Amherst)

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Modeling and Detecting Internet Censorship Events

Elisa Tsai (University of Michigan), Ram Sundara Raman (University of Michigan), Atul Prakash (University of Michigan), Roya Ensafi (University of Michigan)

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