Crowdsourcing identification of license violations

Sanghoon Lee, Daniel M. German, Seung won Hwang, Sunghun Kim

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Free and open source software (FOSS) has created a large pool of source codes that can be easily copied to create new applications. However, a copy should preserve copyright notice and license of the original file unless the license explicitly permits such a change. Through software evolution, it is challenging to keep original licenses or choose proper licenses. As a result, there are many potential license violations. Despite the fact that violations can have high impact on protecting copyright, identification of violations is highly complex. It relies on manual inspections by experts. However, such inspection cannot be scaled up with open source software released daily worldwide. To make this process scalable, we propose the following two methods: use machine-based algorithms to narrow down the potential violations; and guide non-experts to manually inspect violations. Using the first method, we found 219 projects (76.6%) with potential violations. Using the second method, we show that the accuracy of crowds is comparable to that of experts. Our techniques might help developers identify potential violations, understand the causes, and resolve these violations.

Original languageEnglish
Pages (from-to)190-203
Number of pages14
JournalJournal of Computing Science and Engineering
Volume9
Issue number4
DOIs
Publication statusPublished - 2015 Jan 1

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All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications

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