The Prevalence and Impact of Licenses in Open Software Projects
This work provides a large-scale empirical analysis of open-source licensing trends and their association with project activity, which is relevant for developers and researchers studying open-source ecosystems.
The study analyzed over 100 million software projects to examine license distribution and the impact of license changes on project activity. They found that most projects lack a license, permissive licenses are increasingly common, and the effect of switching from restrictive to permissive licenses varies by language ecosystem (e.g., decreased activity in C, increased in Python).
The terms of how publicly available source code can be used are dictated by its license. The license (or its absence), in turn, affects what code the project may reuse and how its code can be (re)used and may also affect external participation and overall activity of the project. We aim to better understand the general state of license distribution overall and within language ecosystems and to investigate if license changes are associated with a noticeable variations of project output. To accomplish that we identify licenses and license types for over 100M software projects and find that most do not contain any license, that permissive licenses represent the bulk of most licenses, and that permissive licensing is representing an increasing proportion of all licenses over time. Restrictive licenses are more likely to be retained, however. There is a great variation among language ecosystems with C-language strongly favoring restrictive licenses. The analysis of license change impact comparing activity within one year of the adoption of the initial and final licenses shows that the change from restrictive to permissive license varies with the ecosystem. C-language ecosystems show reduced activity while Python shows increased activity when comparing restrictive to permissive license transition. Our results demonstrate dramatic changes in license type prevalence over time and find that the effects of license changes may have opposite effects depending on the language ecosystem.