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 language | English |
|---|---|
| Pages (from-to) | 190-203 |
| Number of pages | 14 |
| Journal | Journal of Computing Science and Engineering |
| Volume | 9 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2015 Jan 1 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Engineering(all)
- Computer Science Applications
Cite this
}
Crowdsourcing identification of license violations. / Lee, Sanghoon; German, Daniel M.; Hwang, Seung won; Kim, Sunghun.
In: Journal of Computing Science and Engineering, Vol. 9, No. 4, 01.01.2015, p. 190-203.Research output: Contribution to journal › Article
TY - JOUR
T1 - Crowdsourcing identification of license violations
AU - Lee, Sanghoon
AU - German, Daniel M.
AU - Hwang, Seung won
AU - Kim, Sunghun
PY - 2015/1/1
Y1 - 2015/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85008255741&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85008255741&partnerID=8YFLogxK
U2 - 10.5626/JCSE.2015.9.4.190
DO - 10.5626/JCSE.2015.9.4.190
M3 - Article
AN - SCOPUS:85008255741
VL - 9
SP - 190
EP - 203
JO - Journal of Computing Science and Engineering
JF - Journal of Computing Science and Engineering
SN - 1976-4677
IS - 4
ER -