
Open source software brings benefit to software community, but also introduces legal risks caused by license violations, which result in serious consequences such as lawsuits and financial losses. To mitigate legal risks, some approaches have been proposed to identify licenses, detect license incompatibilities and inconsistencies, and recommend licenses. As far as we know, however, there is no prior work to understand modification terms in open source licenses or to detect and fix violations of modification terms. To bridge this gap, we first empirically characterize modification terms in 47 open source licenses. These licenses all require certain forms of “notice” to describe the modifications made to the original work. Inspired by our study, we then design LIVO to automatically detect and fix violations of modification terms in open source licenses during forking. Our evaluation has shown the effectiveness and efficiency of LIVO. 18 pull requests of fixing modification term violations have received positive responses. 8 have been merged.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
